ets.ndjson

{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","textual","network"],"datasetLabel":"Control Flow Graphs","datasetSubmitTimestamp":"2019-10-19T04:25:17.428Z","terminology":{},"nTables":"3","exampleTableData":{"table1_H_1":"Node 1","table1_H_2":"Node 2","table1_1_H":"Node 1","table1_1_1":"0","table1_1_2":"1","table1_2_H":"Node 2","table1_2_1":"1","table1_2_2":"0","table2_H_1":"ID","table2_H_2":"Label","table2_1_H":"Node1","table2_1_1":"1","table2_1_2":"B1","table2_2_H":"Node 2","table2_2_1":"2","table2_2_2":"B2"},"tabularDetails":["Foreign keys","Empty cells"],"tableNestedExample":"","tableMisc":"The graph could be described as an adjacency matrix","TablesViewProtest":"","hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Python","reflections":"It would limit the exploration of pathfollowing tasks in the graph and would be more foucsed in the attributes of nodes and edges.","ReflectionsViewProtest":"","overallComments":"","browserId":"46139e1cfe99ec8e6532e42d2fa8f551f1aebf299d69fcc758cb8ce563a9a641","surveyVersion":"0.1.1","submitTimestamp":"2019-10-19T04:30:57.740Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","grouped","tabular","spatial"],"datasetLabel":"oceans","datasetSubmitTimestamp":"2019-10-20T23:31:54.568Z","terminology":{},"alternateDefinitions":{},"numDocuments":"1","hardToImagine":"Strongly agree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"I think I would need to discuss this in more detail with a domain expert.  I think they do have conceptual descriptions of ocean currents that would be used through labeling regions, but this data was not provided.","reflections":"I wonder if textual classification of oceans could be fruitful, in the same way that computer vision techniques are used to do textual classification of objects in images.","overallComments":"What an awesome survey!","startTimestamp":"2019-10-20T23:42:04.199Z","browserId":"dd3586dca46b65a99add9db4c9a0e11d5d2141b4303e31d973358f33f9bf0678","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:42:04.279Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","grouped","tabular"],"datasetLabel":"Buildings Data","datasetSubmitTimestamp":"2019-10-20T23:42:48.592Z","terminology":{},"alternateDefinitions":{},"numDocuments":"1","textualDetails":"Delimiters","textualMisc":"Metadata about the buildings and devices","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Strongly agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","softwareList":"It's in CSV format already","startTimestamp":"2019-10-20T23:46:58.249Z","browserId":"cb6c07240619f07a026a83b9bf3685daf1d6b704693d287443149c53ed4b5fb1","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:46:58.249Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Sometimes","nativeRawData":"Moderately inaccurate","otherPriors":["media","textual","grouped","tabular","spatial"],"datasetLabel":"clinical","datasetSubmitTimestamp":"2019-10-20T23:44:53.071Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"≥3","edgeClassCount":"≥3","edgeDirection":"Mixed","exampleNetwork":{"nodes":[{"label":"Please change"},{"label":"this example"},{"label":"network"}],"edges":[{"source":0,"target":1,"directed":true},{"source":1,"target":2,"directed":true}]},"networkDetails":["Hierarchy","Connected"],"hardToImagine":"Disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Strongly disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Neither agree nor disagree","softwareList":"python","reflections":"it would be good to get a sense what the design requirements are. Why we do need to care dataset as network? and so what?","startTimestamp":"2019-10-20T23:48:41.585Z","browserId":"93e24544a5f570926ded576d235f14b257ab2947c24179af74cb6bcddfe0859a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:48:41.586Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Sometimes","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["textual","grouped","spatial","tabular","network"],"datasetLabel":"clinical","datasetSubmitTimestamp":"2019-10-20T23:44:53.071Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"ImageSequences","hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-20T23:50:57.802Z","browserId":"93e24544a5f570926ded576d235f14b257ab2947c24179af74cb6bcddfe0859a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:50:57.803Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","grouped","media","spatial"],"datasetLabel":"VolumeVis","datasetSubmitTimestamp":"2019-10-20T23:40:10.603Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"≥3","edgeDirection":"Undirected","networkDetails":["Cycles","Parallel Edges"],"networkMisc":"Different types of interactions encoded in the edges","hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Strongly disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Strongly disagree","softwareList":"Modified Raytracer","startTimestamp":"2019-10-20T23:51:37.407Z","browserId":"b47ed5512c5bebf94c991ab87db47c89238ac2c312da8c2ff1dfd65bfe5216a4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:51:37.410Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","grouped","tabular"],"datasetLabel":"Happiness city survey","datasetSubmitTimestamp":"2019-10-20T23:49:45.777Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"Videos","mediaMisc":"The data could include media captured by phones to justify the ratings they give.","hardToImagine":"Strongly disagree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Disagree","needsNewData":"Strongly agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Agree","softwareList":"The current survey is mailed out and returned by mail.  If media were used, it would have to be distributed on the web, but that wouldn’t be too bad.\n\nAlternatively, the participants could supply an address, and then we’d need a service that would map addresses to media taken near it, like google maps.","reflections":"I tend to do more machine learning and I use existing datasets and I think of them as static.  But thinking about the media that I wish I had made me realize that for a given task, it can be really practical to increase the amount of media gathered.","startTimestamp":"2019-10-20T23:56:00.889Z","browserId":"8c3d71146469d06a0b729bb4e80ddf4a0324468032cd54d14543b92e0ca3e0da","surveyVersion":"1.0.0","submitTimestamp":"2019-10-20T23:56:00.890Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","spatial","grouped","network","textual"],"datasetLabel":"VAST Challenge 2006","datasetSubmitTimestamp":"2019-10-20T23:59:17.192Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"N/A","hardToImagine":"Strongly agree","newQuestions":"Strongly disagree","inaccurate":"Strongly agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Not sure what this would mean for a free text dataset","startTimestamp":"2019-10-21T00:01:17.724Z","browserId":"cb6c07240619f07a026a83b9bf3685daf1d6b704693d287443149c53ed4b5fb1","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T00:01:17.725Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","grouped","tabular","spatial"],"datasetLabel":"OD","datasetSubmitTimestamp":"2019-10-21T18:39:57.546Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Thousands","textualDetails":"NaturalLanguage","textualMisc":"small comments < 120 words","hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Disagree","planToReshape":"Agree","hardInPractice":"Disagree","softwareList":"Python, nlp techniques","overallComments":"I believe that the survey should contain more examples to better understand the objective of the survey","startTimestamp":"2019-10-21T18:46:34.366Z","browserId":"0fdd6c2f7634a888c5fb942476d9a0875c5b3a1d302be5e54f07673f101f0c53","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T18:46:34.417Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["tabular","grouped","network","media","textual"],"datasetLabel":"Case: Image-Text","datasetSubmitTimestamp":"2019-10-21T18:53:08.645Z","terminology":{},"alternateDefinitions":{},"nDimensions":"2","SpatialNuances":[],"spatialDetails":"Points","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Disagree","hardInPractice":"Disagree","startTimestamp":"2019-10-21T19:00:29.272Z","browserId":"a9cf93a719b65d936d2c9b245aeb9d429d78cc4f57e6fe1abff05ae2702a1670","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T19:02:01.284Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","spatial","tabular"],"datasetLabel":"OD","datasetSubmitTimestamp":"2019-10-21T18:43:51.221Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Tens","textualDetails":"NaturalLanguage","textualMisc":"like a little social network where you can describe from where you take a taxi and where you are going.","hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Strongly disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Strongly agree","softwareList":"NLP, Python","reflections":"the data in this way is more complicated to describe, but in the real world is more offen.","overallComments":"yes, maybe more options to decribe datasets","startTimestamp":"2019-10-21T19:03:06.652Z","browserId":"6c9c79059edc7172920c516c9859ac99510257d478b42fed2e5f34907e239eb7","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T19:03:08.493Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","network","grouped","spatial","tabular"],"datasetLabel":"firstnames","datasetSubmitTimestamp":"2019-10-21T18:29:14.822Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"1","mediaDetails":"Videos","hardToImagine":"Disagree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Agree","hardInPractice":"Strongly disagree","softwareList":"Tableau, D3, Windows 10 Game toolbar to record a video","reflections":"I am not sure if I understood the task correctly. Basically, I imagined I would use the geographic and time aspects of the data to create a animated choropleth map, which is a video medium. \nI would look at the video but not think about it differently than as spatiotemporal data.\nMaybe because I am not specialized in media analysis.\n\nI didn't understand the question \"The dataset I described in the previous \"Data Details\" section could only be created with additional data collection or discovery.\"","overallComments":"I didn't understand the question \"The dataset I described in the previous \"Data Details\" section could only be created with additional data collection or discovery.\"","startTimestamp":"2019-10-21T19:06:30.026Z","browserId":"4be15faaa0823a96f1ccd08ba2be58802f2c0c2f506864146a17939a2f8e97ed","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T19:06:30.690Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","spatial","tabular"],"datasetLabel":"Meterlogical Data","datasetSubmitTimestamp":"2019-10-21T19:20:54.598Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"≥3","edgeClassCount":"1","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Temperature"},{"label":"Max"},{"label":"Min"},{"label":"Avg"},{"label":"11.1"},{"label":"13.2"},{"label":"12.1"}],"edges":[{"source":0,"target":2,"directed":true},{"source":0,"target":1,"directed":true},{"source":0,"target":3,"directed":true},{"source":1,"target":5,"directed":true},{"source":2,"target":4,"directed":true},{"source":3,"target":6,"directed":true}]},"networkDetails":["Hierarchy","Connected"],"networkMisc":"List-like","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"A time-series as a network is essentially a linked-list: accordingly any language that supports linked lists would work fine, C++, Python, Java","reflections":"Oftentimes, for certain types of time-series meteorological data is collected from one sensor that logs three values at one time stamp: min, max, avg. This does provoke an idea that data at certain time steps are related and can be related in a \"tree like structure\" but it also causes problems in visualizing the data because metadata is encoded along with data. This may muddle the purity of visualizing the data by itself. ","overallComments":"This was fun. Really made me think outside the box about my tabular data. Really makes me want to be creative with my data representations.","startTimestamp":"2019-10-21T19:44:45.045Z","browserId":"c3ffa2e066409c4faac462890c7976e7e92d228f60c2e8360e5dbe459acb33c0","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T19:44:45.136Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Sometimes","nativeRawData":"Moderately inaccurate","otherPriors":["textual","grouped","tabular","network","media"],"datasetLabel":"mediatransparency","datasetSubmitTimestamp":"2019-10-21T21:02:02.384Z","terminology":{},"alternateDefinitions":{},"nDimensions":"1","SpatialNuances":["Temporal"],"spatialDetails":"Intervals","hardToImagine":"Strongly disagree","newQuestions":"Strongly agree","inaccurate":"Disagree","useful":"Strongly agree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Agree","hardInPractice":"Strongly disagree","softwareList":"Excel, R","startTimestamp":"2019-10-21T21:09:07.753Z","browserId":"4be15faaa0823a96f1ccd08ba2be58802f2c0c2f506864146a17939a2f8e97ed","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T21:09:07.757Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","network","tabular","spatial"],"datasetLabel":"car availability","datasetSubmitTimestamp":"2019-10-21T21:52:41.989Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"2","mediaDetails":"RasterImages","mediaMisc":"One way to view this data would be processed into maps -- where are the cars? how long does it take to walk to them?","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Strongly agree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Strongly agree","softwareList":"webgl","reflections":"This strikes me as a processing into output formats, rather than input formats","startTimestamp":"2019-10-21T22:46:58.157Z","browserId":"ebdf47b78855bf85ef82af5327de2111b8a2b42022725652c5405b1082a0d64d","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T22:46:58.158Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","textual","media"],"datasetLabel":"Interview Corpus","datasetSubmitTimestamp":"2019-10-21T22:43:49.219Z","terminology":{},"alternateDefinitions":{},"SpatialViewProtest":"This data is inteview notes. While I could imagine temporal aspects, I just don't see how to make this fit","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Strongly agree","useful":"Strongly disagree","moreLikely":"Agree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-21T22:49:55.672Z","browserId":"ebdf47b78855bf85ef82af5327de2111b8a2b42022725652c5405b1082a0d64d","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T22:49:55.672Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","spatial","grouped","tabular"],"datasetLabel":"interactions","datasetSubmitTimestamp":"2019-10-21T22:49:44.338Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"MultiDimensionalImages","hardToImagine":"Strongly agree","newQuestions":"Agree","inaccurate":"Agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","softwareList":"Python","startTimestamp":"2019-10-21T22:52:08.707Z","browserId":"4f21edfd34b11c2750f26df2bf0613e040b6980436f7edd760b6f132b3c0afc2","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T22:52:08.707Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["grouped","textual","network","spatial","tabular"],"datasetLabel":"User Login Logs","datasetSubmitTimestamp":"2019-10-21T21:34:18.120Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"RasterImages","hardToImagine":"Agree","newQuestions":"Neither agree nor disagree","inaccurate":"Disagree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Neither agree nor disagree","softwareList":"javascript","startTimestamp":"2019-10-21T22:54:54.636Z","browserId":"5a24499e754975f0b2040fc3bbd3b7576c9c73ec2b9fd97ba9d457619091dc37","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T22:54:54.636Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","spatial","grouped","tabular","media"],"datasetLabel":"interactions","datasetSubmitTimestamp":"2019-10-21T22:49:44.338Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"≥3","edgeClassCount":"≥3","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"action 1"},{"label":"action 2"},{"label":"action 3"},{"label":"id"}],"edges":[{"source":0,"target":1,"directed":true},{"source":1,"target":2,"directed":true}]},"networkDetails":["Hierarchy","Connected"],"hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Java","startTimestamp":"2019-10-21T22:56:28.568Z","browserId":"4f21edfd34b11c2750f26df2bf0613e040b6980436f7edd760b6f132b3c0afc2","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T22:56:28.568Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","grouped","spatial","tabular"],"datasetLabel":"Transactions","datasetSubmitTimestamp":"2019-10-21T23:10:44.207Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Strongly agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-21T23:14:05.719Z","browserId":"4fe574502d698fdb9a5b013c22a1e08f97e1063c8f839ecff2094c131fedaf46","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:14:05.720Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Always","nativeRawData":"Accurate","otherPriors":["textual","network","grouped","media","spatial"],"datasetLabel":"Bike sharing","datasetSubmitTimestamp":"2019-10-21T23:10:47.329Z","terminology":{},"alternateDefinitions":{},"nTables":"1","exampleTableData":{"table1_H_1":"City","table1_H_2":"Amount ","table1_1_H":"","table1_1_1":"London","table1_1_2":"1090","table1_2_H":"","table1_2_1":"Paris","table1_2_2":"100","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Multidimensional tables"],"hardToImagine":"Strongly disagree","newQuestions":"Strongly disagree","inaccurate":"Strongly disagree","useful":"Strongly agree","moreLikely":"Strongly disagree","needsNewData":"Strongly agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-21T23:14:07.286Z","browserId":"8013cc382aa7d885ad101d387b285a8fd90bd01fce235199cdf566ee37b9077a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:14:07.286Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["media","grouped","spatial","tabular"],"datasetLabel":"Lisbon Renting Market","datasetSubmitTimestamp":"2019-10-21T23:11:32.692Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"1","edgeDirection":"Undirected","exampleNetwork":{"nodes":[{"label":"Belem"},{"label":"T1"},{"label":"Arroios"}],"edges":[{"source":2,"target":1,"directed":false}]},"networkDetails":["Hierarchy","Connected"],"hardToImagine":"Strongly agree","newQuestions":"Strongly disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"Python","startTimestamp":"2019-10-21T23:16:20.661Z","browserId":"241edf590dd2b1ff07101b0ba842f61ed6d87e60a2db1da71252aab9c33bb0cd","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:16:20.662Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","network","spatial","textual","tabular"],"datasetLabel":"CitiBikeData","datasetSubmitTimestamp":"2019-10-21T23:21:19.110Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"1","mediaDetails":"MultiDimensionalImages","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Neither agree nor disagree","planToReshape":"Agree","hardInPractice":"Neither agree nor disagree","softwareList":"Python, Javascript, React","startTimestamp":"2019-10-21T23:23:36.386Z","browserId":"00b1727fd43027d708c1beb7d04bf691b04232f3fd84e98522b67cd86fd567ed","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:23:36.386Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","textual","tabular"],"datasetLabel":"Student grades (fuzzy)","datasetSubmitTimestamp":"2019-10-21T23:21:42.631Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"2","edgeClassCount":"1","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Tom"},{"label":"Jane"},{"label":"Good"},{"label":"Poor"},{"label":"Fairly good"}],"edges":[{"source":1,"target":4,"directed":true},{"source":0,"target":2,"directed":true},{"source":0,"target":4,"directed":true},{"source":1,"target":3,"directed":true}]},"networkDetails":[],"hardToImagine":"Disagree","newQuestions":"Strongly agree","inaccurate":"Strongly disagree","useful":"Strongly agree","moreLikely":"Strongly agree","needsNewData":"Neither agree nor disagree","planToReshape":"Disagree","hardInPractice":"Strongly disagree","softwareList":"JavaScript, Json, d3","startTimestamp":"2019-10-21T23:27:44.134Z","browserId":"7d1e6b86c54eddb211a0d773909bca88b82b01e2c47c0702cadd0536a43838d6","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:27:44.135Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["textual","spatial","grouped","tabular","network"],"datasetLabel":"Blooddata","datasetSubmitTimestamp":"2019-10-21T23:28:25.655Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"VectorImages","hardToImagine":"Strongly agree","newQuestions":"Neither agree nor disagree","inaccurate":"Strongly agree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Agree","startTimestamp":"2019-10-21T23:32:28.727Z","browserId":"9362dd10a4bb71c03736c5f301a4af54beab2f848a69b95bd78c8b1bd54e5f76","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:32:28.727Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["textual","spatial","grouped","tabular","network"],"datasetLabel":"Gene expression ","datasetSubmitTimestamp":"2019-10-21T23:45:45.393Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"2","mediaDetails":"RasterImages","hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Agree","startTimestamp":"2019-10-21T23:52:19.373Z","browserId":"6572ec0901ea70e4cde3f50374a299028a6be6ca4ee80b3a8d5ee66b91138676","surveyVersion":"1.0.0","submitTimestamp":"2019-10-21T23:52:19.377Z"}
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{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["spatial","network","grouped","tabular","media"],"datasetLabel":"Image","datasetSubmitTimestamp":"2019-10-22T18:35:34.613Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Hundreds","textualDetails":"NaturalLanguage","textualMisc":"Correlated token rule ","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Strongly disagree","needsNewData":"Strongly agree","planToReshape":"Strongly disagree","hardInPractice":"Agree","softwareList":"Python, BERT-based method","startTimestamp":"2019-10-22T19:12:03.832Z","browserId":"c75971124c7f76d2b5d6fb4576fec722a14a1d4245fe7d9bf32a92b70323d4cf","surveyVersion":"1.0.0","submitTimestamp":"2019-10-22T19:12:03.833Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","spatial","grouped","tabular"],"datasetLabel":"Politicians","datasetSubmitTimestamp":"2019-10-22T19:10:17.680Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","mediaMisc":"Images, audio, video of politicians \n\nResolution ","hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Strongly disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Strongly agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","softwareList":"I would not transform existing dataset but rather collect additional data, including politicians’ images, speech transcripts or recordings, videos, etc. ","startTimestamp":"2019-10-22T19:13:56.499Z","browserId":"683387f8dc921a62ba4b3e41818f8a4fcd77feb76f9bb14565dcf7e9da85dbcd","surveyVersion":"1.0.0","submitTimestamp":"2019-10-22T19:13:56.501Z"}
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{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","network","textual","tabular","grouped"],"datasetLabel":"US Census","datasetSubmitTimestamp":"2019-10-22T19:27:40.094Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","hardToImagine":"Strongly disagree","newQuestions":"Strongly disagree","inaccurate":"Strongly disagree","useful":"Strongly disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly agree","hardInPractice":"Strongly disagree","softwareList":"D3","reflections":"This is weird.  I think of the data not as media, but I'm actively trying to turn it into media.","startTimestamp":"2019-10-22T19:33:03.317Z","browserId":"9c182a07c3e2545d6d3a00e481b578d189692e47c9290235a6ff5bebe7840d00","surveyVersion":"1.0.0","submitTimestamp":"2019-10-22T19:33:03.320Z"}
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{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","media","grouped","tabular","textual"],"datasetLabel":"Board Game Geek","datasetSubmitTimestamp":"2019-10-22T23:22:50.142Z","terminology":{},"alternateDefinitions":{},"nDimensions":"1","SpatialNuances":["Temporal"],"hardToImagine":"Strongly disagree","newQuestions":"Agree","inaccurate":"Strongly disagree","useful":"Strongly agree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Agree","hardInPractice":"Agree","softwareList":"Python (to collect the data via bgg's xml API), Trifacta to clean and curate, and Tableau to visualize","startTimestamp":"2019-10-22T23:26:19.234Z","browserId":"2e995fd239551e008e10a3981e4e7682a01c13fe592510931eea1b28f8887a34","surveyVersion":"1.0.0","submitTimestamp":"2019-10-22T23:26:19.234Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","grouped","textual","tabular"],"datasetLabel":"DAD","datasetSubmitTimestamp":"2019-10-22T18:47:53.508Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"2","edgeClassCount":"2","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Obstructive apnea"},{"label":"Lung cancer"},{"label":"Diseases of the respiratory system"}],"edges":[{"source":2,"target":1,"directed":true},{"source":2,"target":0,"directed":true}]},"networkDetails":["Hierarchy","Connected"],"hardToImagine":"Disagree","newQuestions":"Disagree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Disagree","hardInPractice":"Neither agree nor disagree","softwareList":"Could be displayed as a tree, i would hire someone","startTimestamp":"2019-10-22T23:49:21.595Z","browserId":"e5faf7eb60cc0433397422fe1e888648cbbe390cc759d01897a2c38f9070aed8","surveyVersion":"1.0.0","submitTimestamp":"2019-10-22T23:49:21.596Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","tabular","spatial","network"],"datasetLabel":"Deep Weather","datasetSubmitTimestamp":"2019-10-21T23:40:05.876Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"Audio","hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-23T00:04:27.264Z","browserId":"3f123199d32a6a43d3e3011d840502e1d614ff6987a49d19a6526639c08f72c4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T00:04:27.265Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Moderately inaccurate","otherPriors":["spatial","network","textual","grouped","media"],"datasetLabel":"MusicData","datasetSubmitTimestamp":"2019-10-23T00:08:51.995Z","terminology":{},"alternateDefinitions":{},"nTables":"Hundreds","exampleTableData":{"table1_H_1":"","table1_H_2":"","table1_1_H":"","table1_1_1":"","table1_1_2":"","table1_2_H":"","table1_2_1":"","table1_2_2":"","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Multidimensional tables"],"hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Strongly agree","planToReshape":"Agree","hardInPractice":"Disagree","softwareList":"KNIME","overallComments":"Provide multiple examples to nourish creativity","startTimestamp":"2019-10-23T00:14:29.335Z","browserId":"063f91315193a7b4bae9a9dd3c09bedfba0c32109247422c3152b70e75227e22","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T00:14:29.578Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","tabular","spatial","network"],"datasetLabel":"Deep Weather","datasetSubmitTimestamp":"2019-10-21T23:40:05.876Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"Audio","hardToImagine":"Strongly disagree","newQuestions":"Strongly disagree","inaccurate":"Strongly agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","startTimestamp":"2019-10-23T00:20:29.799Z","browserId":"3f123199d32a6a43d3e3011d840502e1d614ff6987a49d19a6526639c08f72c4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T00:20:29.800Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["media","network","spatial","grouped","tabular"],"datasetLabel":"AMT results","datasetSubmitTimestamp":"2019-10-23T01:16:13.086Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Hundreds","textualDetails":"NaturalLanguage","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-23T01:18:27.095Z","browserId":"a36912be4f1ad0676e100f449b196bccab64c908a4fab2745739a0878b36e2c4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T01:18:27.095Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Moderately inaccurate","otherPriors":["network","grouped","tabular","textual","spatial"],"datasetLabel":"health-related discussion forum data","datasetSubmitTimestamp":"2019-10-23T05:00:12.962Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"ImageSequences","hardToImagine":"Neither agree nor disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Disagree","softwareList":"Python","overallComments":"In reading the initial description for this section, I realized that what I do with discussion forum data might be considered media, so it was a bit difficult to answer the questions (it seemed to suggest that I do not do those things now).","startTimestamp":"2019-10-23T05:04:41.077Z","browserId":"c5ee04e5f8601ab1554e1991406686815fd2c62bbd2331ee6e157402ae5c3aa2","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T05:04:41.253Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Sometimes","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["network","grouped","textual","spatial","media"],"datasetLabel":"corpus of historical documents","datasetSubmitTimestamp":"2019-10-23T05:19:00.039Z","terminology":{},"alternateDefinitions":{},"nTables":"1","exampleTableData":{"table1_H_1":"Author","table1_H_2":"Location","table1_1_H":"1","table1_1_1":"John Smith","table1_1_2":"Paris, France","table1_2_H":"2","table1_2_1":"Sarah Williams","table1_2_2":"Seoul, South Korea","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Multidimensional tables","Foreign keys"],"hardToImagine":"Neither agree nor disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Agree","hardInPractice":"Agree","softwareList":"Python","overallComments":"I had a hard time, actually, thinking of the dataset as tabular in a way that I had not previously thought of it as tabular (which I believe was the intent of this survey). The questions did not seem to specifically account for this possibility as distinct from considering it as tabular for the first time.","startTimestamp":"2019-10-23T05:30:00.171Z","browserId":"c5ee04e5f8601ab1554e1991406686815fd2c62bbd2331ee6e157402ae5c3aa2","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T05:30:00.330Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Very inaccurate","otherPriors":["textual","network","grouped","spatial","tabular"],"datasetLabel":"Birds migration","datasetSubmitTimestamp":"2019-10-23T15:23:29.428Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"ImageSequences","hardToImagine":"Strongly disagree","newQuestions":"Agree","inaccurate":"Strongly disagree","useful":"Strongly agree","moreLikely":"Agree","needsNewData":"Disagree","planToReshape":"Agree","hardInPractice":"Disagree","softwareList":"Programming \nTableau","overallComments":"Good alternative thinking ","startTimestamp":"2019-10-23T15:26:33.117Z","browserId":"6f117cfd2c3935bcb1f6189838eeaff1c86d5693d29636c6a8250ee6d42540f5","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T15:26:35.436Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["textual","spatial","grouped","tabular","network"],"datasetLabel":"MLPipelines","datasetSubmitTimestamp":"2019-10-23T15:41:31.129Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"2","mediaDetails":"RasterImages","hardToImagine":"Neither agree nor disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Agree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-23T15:44:42.354Z","browserId":"6359fe14e8a06149c62171ebbc4c339b33c177c4647bfe5d88d4dcd6f908fc5d","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T15:44:42.354Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","network","grouped"],"datasetLabel":"instruction combiner rules","datasetSubmitTimestamp":"2019-10-23T15:41:29.217Z","terminology":{},"alternateDefinitions":{},"nTables":"1","exampleTableData":{"table1_H_1":"node a","table1_H_2":"node b","table1_1_H":"node a","table1_1_1":"","table1_1_2":"","table1_2_H":"node b","table1_2_1":"","table1_2_2":"","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Empty cells"],"hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Strongly agree","useful":"Agree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","softwareList":"I would build an adjacency matrix.","reflections":"Inefficient to run","startTimestamp":"2019-10-23T15:45:12.614Z","browserId":"75ebda9ab4a242f16101a1bc03ee96099ed68e4387e23603a630f486585d171b","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T15:45:12.615Z"}
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{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","tabular","network","grouped","spatial"],"datasetLabel":"Trace data","datasetSubmitTimestamp":"2019-10-23T15:52:54.711Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","mediaMisc":"Animation of the workers, e.g. showing calls","hardToImagine":"Neither agree nor disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Disagree","softwareList":"Using the Information from the Trace data, run an animation implemented with JS and d3 and record it ","startTimestamp":"2019-10-23T15:58:22.342Z","browserId":"5efbb253d8c3d6af3fb52e51926da3549260ff6855fa7796fd5aa394d01eac56","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T15:58:22.344Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","tabular","grouped","spatial","media"],"datasetLabel":"Stroke-Collection","datasetSubmitTimestamp":"2019-10-23T16:23:36.792Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Hundreds","textualDetails":"Typography","hardToImagine":"Neither agree nor disagree","newQuestions":"Agree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Word-like software","reflections":"The only possible way I can imagine is that a doctor writes a description of the dataset","startTimestamp":"2019-10-23T16:28:12.510Z","browserId":"e8dcae9708c9c6904ba39c654fd8d756d53f8636c9385f356cf1b08e6576620a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T16:28:12.510Z"}
{"targetType":"grouped","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","textual","spatial"],"datasetLabel":"HS Stats","datasetSubmitTimestamp":"2019-10-23T16:14:52.648Z","terminology":{},"alternateDefinitions":{},"numGroups":"1","hardToImagine":"Agree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Strongly agree","planToReshape":"Disagree","hardInPractice":"Agree","startTimestamp":"2019-10-23T16:44:32.184Z","browserId":"2e995fd239551e008e10a3981e4e7682a01c13fe592510931eea1b28f8887a34","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T16:44:32.184Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","tabular","spatial","grouped"],"datasetLabel":"Browser stats","datasetSubmitTimestamp":"2019-10-23T16:34:14.701Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"N/A","hardToImagine":"Strongly agree","newQuestions":"Strongly disagree","inaccurate":"Strongly agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-23T16:50:43.761Z","browserId":"2e995fd239551e008e10a3981e4e7682a01c13fe592510931eea1b28f8887a34","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T16:50:43.761Z"}
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{"targetType":"grouped","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","spatial","media","network"],"datasetLabel":"RGB-D","datasetSubmitTimestamp":"2019-10-23T17:05:33.994Z","terminology":{},"alternateDefinitions":{},"numGroups":"Tens","groupedDetails":"Partitions","hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","softwareList":"Cluster into connected components","startTimestamp":"2019-10-23T17:09:06.769Z","browserId":"b47ed5512c5bebf94c991ab87db47c89238ac2c312da8c2ff1dfd65bfe5216a4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T17:09:06.770Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","grouped","network"],"datasetLabel":"housing models","datasetSubmitTimestamp":"2019-10-23T16:08:05.252Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"N/A","mediaDetails":"RasterImages","hardToImagine":"Strongly agree","newQuestions":"Neither agree nor disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"Ffmpeg","reflections":"Media is a very broad term","overallComments":"Data is not just discrete. There are functions and geometric objects as well","startTimestamp":"2019-10-23T17:28:21.687Z","browserId":"75c858fdf0dab1057fd2e3f39a9a6feaef4200aa1b10b0b173de762ad07d0838","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T17:28:22.855Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","textual","media","grouped","spatial"],"datasetLabel":"EEG experiment","datasetSubmitTimestamp":"2019-10-23T17:43:46.914Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"2","edgeClassCount":"2","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Response2"},{"label":"this example"},{"label":"network"},{"label":"Response 1"}],"edges":[{"source":0,"target":1,"directed":true},{"source":1,"target":2,"directed":true},{"source":3,"target":0,"directed":true}]},"networkDetails":["Hierarchy","Connected"],"hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Excel, Matlab","reflections":"It would be hard to publish data that was analyzed or presented in this way because it would be so unusual in the field","startTimestamp":"2019-10-23T17:47:27.532Z","browserId":"b91e2f00dd3f1f96df819df833a07510a3c4938c8400a39bdf0fd4861a67b733","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T17:47:27.535Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["spatial","media","grouped","textual","tabular"],"datasetLabel":"IMDB","datasetSubmitTimestamp":"2019-10-23T17:43:26.375Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"≥3","edgeClassCount":"≥3","edgeDirection":"Mixed","exampleNetwork":{"nodes":[{"label":"John doe"},{"label":"network"}],"edges":[{"source":0,"target":0,"directed":true}]},"networkDetails":[],"hardToImagine":"Strongly disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Disagree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Strongly disagree","softwareList":"SQL","startTimestamp":"2019-10-23T17:46:02.561Z","browserId":"3b51ea4d5d06fb881b3d72e9e1ed41600b05e2ad988b47b7cb5e2f7db9a9617f","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T17:46:02.562Z"}
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{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","textual","grouped","tabular","spatial"],"datasetLabel":"Social Science dataset","datasetSubmitTimestamp":"2019-10-23T17:52:53.219Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","hardToImagine":"Neither agree nor disagree","newQuestions":"Agree","inaccurate":"Agree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-23T17:55:45.236Z","browserId":"25a4365ddf9a4a5b785c72421b5bd7b3ff2f70cb24deaf5345f87e952ef40b18","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T17:55:45.237Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","tabular","grouped","network"],"datasetLabel":"Chimera","datasetSubmitTimestamp":"2019-10-23T18:00:13.150Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Thousands","textualDetails":"Delimiters","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"python","startTimestamp":"2019-10-23T18:05:13.085Z","browserId":"129b8bbac2ba06ac7271a3c0ce329c6b78325c4a219c457bd1bbd82f65962d7c","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T18:05:13.086Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","grouped","tabular","media","spatial"],"datasetLabel":"Multichannel microscopy","datasetSubmitTimestamp":"2019-10-23T18:10:01.692Z","terminology":{},"alternateDefinitions":{},"numDocuments":"1","textualDetails":"Delimiters","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-23T18:15:00.875Z","browserId":"39b67742194d57da895a07200d0e83c05251c305d37e8ed85dd9acfaa15bf549","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T18:15:00.877Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","tabular","spatial","grouped","media"],"datasetLabel":"Animals","datasetSubmitTimestamp":"2019-10-23T18:11:22.255Z","terminology":{},"alternateDefinitions":{},"numDocuments":"0","hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"Python","startTimestamp":"2019-10-23T18:25:23.467Z","browserId":"2e995fd239551e008e10a3981e4e7682a01c13fe592510931eea1b28f8887a34","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T18:25:23.467Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","grouped","spatial"],"datasetLabel":"<no label provided>","datasetSubmitTimestamp":"2019-10-23T22:23:11.271Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"Audio","hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Disagree","useful":"Neither agree nor disagree","moreLikely":"Agree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Python\nSound producing library","reflections":"Transforming into audio as I thought of it now would probabky make the analysis less efficient /  more time-consuming.","startTimestamp":"2019-10-23T22:37:49.683Z","browserId":"21629618ede83c4413409f57c82da49a60a85efa2c0019d4a072c324d52d3e84","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T22:37:49.685Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["textual","network","spatial","grouped","tabular"],"datasetLabel":"disease_data","datasetSubmitTimestamp":"2019-10-23T23:54:06.838Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"2","mediaDetails":"Videos","hardToImagine":"Disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Strongly disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Agree","reflections":"It is possible to turn such data into media, but it is not general consumed in this format because it needs to work with existing analytic processes.","startTimestamp":"2019-10-23T23:55:55.084Z","browserId":"c3d404af8bb3fee747bcff66972a2e881982479652141fc032fc7119f1a01996","surveyVersion":"1.0.0","submitTimestamp":"2019-10-23T23:55:55.084Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","spatial","grouped","tabular"],"datasetLabel":"mtl","datasetSubmitTimestamp":"2019-10-24T00:23:21.754Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"VectorImages","mediaMisc":"I have displayed this data by mapping some of it to the three RGB color channels and making a heatmap, but I don't consider the data itself to \"be\" media or \"have\" media.","hardToImagine":"Strongly agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Agree","startTimestamp":"2019-10-24T00:25:21.275Z","browserId":"952d801211a08b9f78aa02c8ee5cc30cc72e55e891753431767db433e54b6f60","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T00:25:21.275Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Neither inaccurate nor accurate","otherPriors":["textual","network","tabular","spatial","grouped"],"datasetLabel":"London cars","datasetSubmitTimestamp":"2019-10-24T00:24:15.766Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"N/A","hardToImagine":"Strongly disagree","newQuestions":"Strongly disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-24T00:27:08.188Z","browserId":"651c2c86b617645874b7daf3deedac93ed14016a61907b09d71d6ea7c12bc53d","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T00:27:08.189Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","spatial"],"datasetLabel":"MD Simulation","datasetSubmitTimestamp":"2019-10-24T00:47:45.020Z","terminology":{},"alternateDefinitions":{},"nTables":"1","exampleTableData":{"table1_H_1":"Energy","table1_H_2":"Distance","table1_1_H":"Time step 1","table1_1_1":"","table1_1_2":"","table1_2_H":"Time step 2","table1_2_1":"","table1_2_2":"","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Multidimensional tables","Nested cell structures"],"tableNestedExample":"I can imagine that calls can contain lists of interesting amino acid, etc.","tableMisc":"The tabular data would be used to explore extrapolated geometrical or biochemical properties of the simulation.","hardToImagine":"Strongly disagree","newQuestions":"Strongly disagree","inaccurate":"Strongly disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly agree","hardInPractice":"Strongly disagree","softwareList":"Python or Java scripts","startTimestamp":"2019-10-24T00:57:06.441Z","browserId":"e595eea0b8e063621338836da8e3c358d206e876fc5b53e9aee8f37c21fb86ea","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T00:57:06.442Z"}
{"targetType":"grouped","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["network","media","textual","tabular","spatial"],"datasetLabel":"Oct","datasetSubmitTimestamp":"2019-10-24T01:13:40.759Z","terminology":{},"alternateDefinitions":{},"numGroups":"Hundreds","hardToImagine":"Agree","newQuestions":"Strongly disagree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Agree","startTimestamp":"2019-10-24T01:18:07.481Z","browserId":"4252e7d4ddc76ca554ff9b4f466a6bc469596d58c4c482863f2b0f75be2fe8a9","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:18:07.483Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","grouped"],"datasetLabel":"Flight plan","datasetSubmitTimestamp":"2019-10-24T01:23:25.744Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"MultiDimensionalImages","hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Agree","startTimestamp":"2019-10-24T01:25:36.967Z","browserId":"4252e7d4ddc76ca554ff9b4f466a6bc469596d58c4c482863f2b0f75be2fe8a9","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:25:36.969Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","spatial","tabular","network"],"datasetLabel":"MeSH","datasetSubmitTimestamp":"2019-10-23T19:41:03.412Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"ImageSequences","hardToImagine":"Neither agree nor disagree","newQuestions":"Strongly disagree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly agree","planToReshape":"Strongly disagree","hardInPractice":"Neither agree nor disagree","softwareList":"Machine learning,python,JavaScript","reflections":"In my opinion the dataset is too big to represent it as media in a pure sense. While images could help to understand some terms and relations faster than others, many terms in the data might not have a proper equivalent in form of an image.","overallComments":"multidimensional image was red but not defined in the dictionary","startTimestamp":"2019-10-24T01:26:31.008Z","browserId":"41c82e6d9ef089814b29729e10139a36fb9f33d18a9bbbb19a0099df18dede18","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:26:32.822Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately accurate","otherPriors":["network","grouped","tabular","media","spatial"],"datasetLabel":"cryoem","datasetSubmitTimestamp":"2019-10-24T01:26:49.108Z","terminology":{},"alternateDefinitions":{},"numDocuments":"1","textualDetails":"Delimiters","textualMisc":"see pdb format specs","hardToImagine":"Neither agree nor disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly disagree","planToReshape":"Strongly disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-24T01:29:58.534Z","browserId":"9b46988fc87703c1b5dc113c9778e1fbd4b010aa79c788c79a648e1aca9b8899","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:29:58.535Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","textual","grouped","tabular","network"],"datasetLabel":"Multi Network","datasetSubmitTimestamp":"2019-10-24T01:34:26.323Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","hardToImagine":"Strongly disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Agree","startTimestamp":"2019-10-24T01:39:56.682Z","browserId":"a9d7f2765b0a1625dfb5c08885aeeca171942b4fa3422912a996019c7c90a9af","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:39:56.683Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["network","textual","media","grouped","spatial"],"datasetLabel":"heart","datasetSubmitTimestamp":"2019-10-24T01:36:28.163Z","terminology":{},"alternateDefinitions":{},"nTables":"Thousands","exampleTableData":{"table1_H_1":"X","table1_H_2":"Y","table1_1_H":"pressure","table1_1_1":"","table1_1_2":"","table1_2_H":"velocity","table1_2_1":"","table1_2_2":"","table2_H_1":"voxel id","table2_H_2":"pressure","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":["Multidimensional tables"],"hardToImagine":"Neither agree nor disagree","newQuestions":"Disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Disagree","hardInPractice":"Agree","softwareList":"Python, C++ custom libraries","reflections":"I think it could be done, a 3D volume is in some sense just a set of stacked 2D tables, but I'm not sure that is what this question is really getting at.  I'm not sure if it would make sense to flatten volume data into a tabular form, I could believe that there are some derived quantities or meta data that would be useful to view this way, but not sure if transforming raw volume data would be that useful.  I think that is a larger interesting question about this project -- to what extent does it connect with volume data or 3D simulation data, imaging, the sort of things that are common in sci vis?","startTimestamp":"2019-10-24T01:45:12.345Z","browserId":"c505a291ea5517d058390193a07a27537bbbf530372a9f8a14cab51e569fe166","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:45:12.346Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","spatial"],"datasetLabel":"survey-data","datasetSubmitTimestamp":"2019-10-24T01:53:36.108Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Tens","textualDetails":"NaturalLanguage","hardToImagine":"Agree","newQuestions":"Neither agree nor disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Disagree","softwareList":"Python, R","startTimestamp":"2019-10-24T01:57:31.451Z","browserId":"7af6259b2bb1fa15f435b1a8ce73a3201c9acbbc05c726f3d076ed7fe86b40f4","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T01:57:31.453Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular"],"datasetLabel":"SWAT","datasetSubmitTimestamp":"2019-10-24T02:02:40.668Z","terminology":{},"alternateDefinitions":{},"nDimensions":"≥4","SpatialNuances":["Temporal","Abstract Space"],"spatialDetails":"Intervals","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Strongly disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-24T02:05:50.598Z","browserId":"1506ca6b73cda13d5263a1ec1918b7aa13a6534338f70954fe9bac13de3add25","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T02:05:50.601Z"}
{"targetType":"tabular","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","grouped","spatial"],"datasetLabel":"cosmology","datasetSubmitTimestamp":"2019-10-24T14:36:21.728Z","terminology":{},"alternateDefinitions":{},"nTables":"1","exampleTableData":{"table1_H_1":"Type","table1_H_2":"Temperature","table1_1_H":"0","table1_1_1":"Baryonic","table1_1_2":"1000","table1_2_H":"1","table1_2_1":"Dark matter","table1_2_2":"42","table2_H_1":"","table2_H_2":"","table2_1_H":"","table2_1_1":"","table2_1_2":"","table2_2_H":"","table2_2_1":"","table2_2_2":""},"tabularDetails":[],"hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Strongly agree","needsNewData":"Strongly disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","softwareList":"Python, compression and parallelization","startTimestamp":"2019-10-24T14:40:26.983Z","browserId":"af9655e410ecaddcfe0c1f01b482dfc3809fc43457a915429330d8d1719fc9f5","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T14:40:26.988Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["media","grouped","spatial","tabular"],"datasetLabel":"US Energy Consumption","datasetSubmitTimestamp":"2019-10-24T17:59:51.401Z","terminology":{},"alternateDefinitions":{},"numDocuments":"2","textualDetails":"Delimiters","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Disagree","hardInPractice":"Agree","startTimestamp":"2019-10-24T18:02:48.907Z","browserId":"1b8987bac06ab426d0718813cfb162f55a7d008e296ccaa19c3bdaf3b799536a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:02:48.908Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Moderately inaccurate","otherPriors":["spatial","textual","grouped","network","tabular"],"datasetLabel":"MicroarrayCollection","datasetSubmitTimestamp":"2019-10-24T18:09:33.879Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"2","mediaDetails":"ImageSequences","hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Strongly agree","planToReshape":"Disagree","hardInPractice":"Disagree","softwareList":"Python, R, libraries such as bioconductor","startTimestamp":"2019-10-24T18:11:47.115Z","browserId":"f43d48d97fea303d08a3db5da549d8cbd841990f7649b47d6e3934e11980b481","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:11:47.116Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","grouped","tabular","spatial"],"datasetLabel":"Retail","datasetSubmitTimestamp":"2019-10-24T18:09:30.628Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"2","edgeDirection":"Undirected","exampleNetwork":{"nodes":[{"label":"Gmobile"},{"label":"Vericen"},{"label":"Yogurtworld"}],"edges":[{"source":0,"target":1,"directed":false},{"source":2,"target":0,"directed":false}]},"networkDetails":["Connected","Cycles"],"hardToImagine":"Strongly agree","newQuestions":"Agree","inaccurate":"Agree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","softwareList":"SQL, python ","startTimestamp":"2019-10-24T18:22:25.164Z","browserId":"2422f975547324e5954e6c7bae9b70896df38a3990fa52c1ff0ac2c256e2732f","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:22:25.166Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Very inaccurate","otherPriors":["grouped","textual","spatial","media","tabular"],"datasetLabel":"Top Spotify","datasetSubmitTimestamp":"2019-10-24T18:16:03.923Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"1","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Ed Sheeran"},{"label":"Shape of You"},{"label":"Castle on the Hill"},{"label":"Justin Bieber"},{"label":"Baby"}],"edges":[{"source":0,"target":1,"directed":true},{"source":0,"target":2,"directed":true},{"source":3,"target":4,"directed":true}]},"networkDetails":["Hierarchy"],"hardToImagine":"Agree","newQuestions":"Neither agree nor disagree","inaccurate":"Disagree","useful":"Neither agree nor disagree","moreLikely":"Agree","needsNewData":"Strongly disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","softwareList":"Tableau, JS, D3","startTimestamp":"2019-10-24T18:23:40.750Z","browserId":"f76cf39cc45319795a4bc801ec33aa95dbb0ba1ec2a5e7d86f817980dcbf27fc","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:23:40.752Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","media","grouped","spatial","tabular"],"datasetLabel":"NHL History","datasetSubmitTimestamp":"2019-10-24T18:21:36.798Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Hundreds","textualDetails":"FormalLanguage","textualMisc":"Text documents that describe each NHL team's season - the text documents would need to be linked to that team's season in the tabular dataset.","hardToImagine":"Disagree","newQuestions":"Agree","inaccurate":"Strongly disagree","useful":"Agree","moreLikely":"Disagree","needsNewData":"Strongly agree","planToReshape":"Strongly disagree","hardInPractice":"Agree","softwareList":"Python scripting with Pandas toolkit.","startTimestamp":"2019-10-24T18:25:00.153Z","browserId":"5345c224eea60f36492020a7287222178cf06579a12fdc9fcc517caa30a5ed7c","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:25:00.153Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["media","grouped","spatial","tabular","textual"],"datasetLabel":"NHL History","datasetSubmitTimestamp":"2019-10-24T18:21:36.798Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"2","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Please change"},{"label":"this example"},{"label":"network"},{"label":"Teams"},{"label":"Same Division"},{"label":"Same Conference"}],"edges":[{"source":0,"target":1,"directed":false},{"source":1,"target":2,"directed":false},{"source":3,"target":3,"directed":false}]},"networkDetails":["Hierarchy","Supernodes"],"hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Agree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Agree","softwareList":"Maybe Python scripts?","startTimestamp":"2019-10-24T18:29:03.728Z","browserId":"5345c224eea60f36492020a7287222178cf06579a12fdc9fcc517caa30a5ed7c","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T18:29:03.728Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Rarely","nativeRawData":"Moderately inaccurate","otherPriors":["network","grouped","spatial","tabular","textual"],"datasetLabel":"St. Paul Crime","datasetSubmitTimestamp":"2019-10-24T19:00:24.540Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"1","mediaDetails":"VectorImages","hardToImagine":"Disagree","newQuestions":"Neither agree nor disagree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Agree","needsNewData":"Neither agree nor disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Disagree","startTimestamp":"2019-10-24T19:01:56.983Z","browserId":"1b8987bac06ab426d0718813cfb162f55a7d008e296ccaa19c3bdaf3b799536a","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T19:01:56.984Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","spatial","tabular"],"datasetLabel":"Basketball","datasetSubmitTimestamp":"2019-10-24T21:09:09.759Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"MultiDimensionalImages","hardToImagine":"Strongly agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly agree","planToReshape":"Strongly disagree","hardInPractice":"Agree","softwareList":"OpenCV for image and video analysis.","reflections":"Having media data would enable a richer set of analysis but the analysis would have to be a lot more complex.","startTimestamp":"2019-10-24T21:11:49.018Z","browserId":"5381b3a45cd3dd44d047cf5d16f797798e3d05ecb11f99ccb7affde1c40b0411","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T21:11:49.018Z"}
{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["media","textual","grouped","tabular","spatial"],"datasetLabel":"DFF","datasetSubmitTimestamp":"2019-10-24T21:04:03.724Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"2","edgeClassCount":"2","edgeDirection":"Directed","exampleNetwork":{"nodes":[{"label":"Date"},{"label":"Rate"},{"label":"Sentiment"}],"edges":[{"source":1,"target":2,"directed":true}]},"networkDetails":["Connected"],"hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Disagree","useful":"Agree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Disagree","softwareList":"Excel,Illustrator","reflections":"Yeah, a bit of a stretch with my relatively straightforward dataset, but this exercise prodded me into thinking about my annotations as more of a central player in the overall visualization as opposed to a secondary thought or supporting contextual element.","startTimestamp":"2019-10-24T21:12:23.097Z","browserId":"45f90fa296dc00347559150b5a8c52575ff7b6d698574c7faa2ce021ee74bdba","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T21:12:23.098Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","tabular","media","spatial"],"datasetLabel":"Nif","datasetSubmitTimestamp":"2019-10-24T22:42:08.240Z","terminology":{},"alternateDefinitions":{},"TextualViewProtest":"This makes no sense for my data ","hardToImagine":"Strongly agree","newQuestions":"Strongly disagree","inaccurate":"Strongly agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Strongly disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-24T22:45:03.608Z","browserId":"f612967622ba05394a755126f499239062ba24de23863aa87e2d64d21e464c97","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T22:45:03.608Z"}
{"targetType":"textual","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["grouped","tabular"],"datasetLabel":"Odds","datasetSubmitTimestamp":"2019-10-24T22:58:56.171Z","terminology":{},"alternateDefinitions":{},"numDocuments":"Thousands","textualDetails":"Delimiters","hardToImagine":"Agree","newQuestions":"Strongly disagree","inaccurate":"Disagree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Disagree","planToReshape":"Strongly disagree","hardInPractice":"Strongly agree","startTimestamp":"2019-10-24T23:02:34.412Z","browserId":"86131f4665d4791b637adc33de02330d42dbc19e051626f1fa7ed8ee17f71952","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T23:02:34.417Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","textual","grouped","tabular","spatial"],"datasetLabel":"energy","datasetSubmitTimestamp":"2019-10-24T23:20:04.798Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"Videos","hardToImagine":"Strongly disagree","newQuestions":"Strongly agree","inaccurate":"Strongly disagree","useful":"Agree","moreLikely":"Strongly agree","needsNewData":"Neither agree nor disagree","planToReshape":"Agree","hardInPractice":"Strongly disagree","softwareList":"Scatter plots","startTimestamp":"2019-10-24T23:24:32.840Z","browserId":"adc49f617f1517ccd00097f57cec213f4cbabac4b8eff8a55e05139e298ad11b","surveyVersion":"1.0.0","submitTimestamp":"2019-10-24T23:24:32.842Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Moderately inaccurate","otherPriors":["media","grouped","textual","network","tabular"],"datasetLabel":"IMDB","datasetSubmitTimestamp":"2019-10-24T23:58:04.694Z","terminology":{},"alternateDefinitions":{},"nDimensions":"≥4","SpatialNuances":["Temporal"],"spatialDetails":"Areas","spatialMisc":"My responses above are based on attributes like location in which a movie was shot and the year in which it was released etc.","hardToImagine":"Disagree","newQuestions":"Disagree","inaccurate":"Neither agree nor disagree","useful":"Disagree","moreLikely":"Strongly disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Disagree","hardInPractice":"Neither agree nor disagree","softwareList":"For the last question, the difficulty would depend on the attributes I have in the original dataset. For instance, if I don't have lat/long, it would be more challenging to plot the network on a map than it would if I did.\n\nTools: Python","startTimestamp":"2019-10-25T00:03:41.772Z","browserId":"002d8056ed2096318399393dee52ec4da0869d90108b82aa88fac9124a022e05","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T00:03:41.773Z"}
{"targetType":"spatial","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["textual","grouped","tabular"],"datasetLabel":"inferencing survey","datasetSubmitTimestamp":"2019-10-24T23:16:07.879Z","terminology":{},"alternateDefinitions":{},"SpatialViewProtest":"I am finding it very difficult to imagine this dataset as spatial or temporal. There's the geo coordinates of the participants, but that's really irrelevant to the analysis.","hardToImagine":"Strongly agree","newQuestions":"Strongly disagree","inaccurate":"Agree","useful":"Strongly disagree","moreLikely":"Strongly disagree","needsNewData":"Neither agree nor disagree","planToReshape":"Strongly disagree","hardInPractice":"Neither agree nor disagree","startTimestamp":"2019-10-25T00:08:43.012Z","browserId":"666bc30dfbe93b2ed47d56ee06e1bbddafde437f8dba836521442e1836211798","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T00:08:43.012Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["network","grouped","spatial","tabular"],"datasetLabel":"atomistic dataset","datasetSubmitTimestamp":"2019-10-25T00:09:31.593Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"3","mediaDetails":"ImageSequences","hardToImagine":"Disagree","newQuestions":"Disagree","inaccurate":"Disagree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Neither agree nor disagree","hardInPractice":"Strongly disagree","softwareList":"Paraview, VMD, PyMol, VisIt, MegaMol","startTimestamp":"2019-10-25T00:12:33.946Z","browserId":"fe28d474e45519a3af2ff5dd689d80dafcd7016774e8d378c96c2f4d33323390","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T00:12:33.950Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","grouped","network","spatial"],"datasetLabel":"tree","datasetSubmitTimestamp":"2019-10-25T02:17:23.529Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","hardToImagine":"Agree","newQuestions":"Disagree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Agree","planToReshape":"Neither agree nor disagree","hardInPractice":"Neither agree nor disagree","softwareList":"python?\n","startTimestamp":"2019-10-25T02:21:48.329Z","browserId":"8932d2dd6dc2ed178a661b96927be3a4eb329040eb412bfd0bf661f0ef073c54","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T02:21:48.330Z"}
{"targetType":"media","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["spatial","grouped","tabular"],"datasetLabel":"Tool inventory","datasetSubmitTimestamp":"2019-10-11T16:29:13.761Z","terminology":{},"alternateDefinitions":{},"colorChannelCount":"≥4","mediaDetails":"MultiDimensionalImages","mediaMisc":"small thumbnails","hardToImagine":"Strongly agree","newQuestions":"Neither agree nor disagree","inaccurate":"Strongly agree","useful":"Disagree","moreLikely":"Neither agree nor disagree","needsNewData":"Strongly agree","planToReshape":"Disagree","hardInPractice":"Strongly agree","softwareList":"I would hire volunteers to take pictures of the tools and their storage locations and use the images as thumbnails so that users would know what it looked like (e.g. we have 13 hammers and here is a picture of a hammer)., If I could ONLY use media, I would have to convert each amount into an image, which would require the user to manually count the images (much like they would have to manually count the hammers) - this would not save time.","reflections":"Trying to visualize this quantitative data (tool counts) as media (images) would not help the user. However, it may be helpful to add media to augment the existing data, like by adding a tool thumbnail or a small map (media?) of the location of the tool in the warehouse. ","startTimestamp":"2019-10-25T16:41:40.264Z","browserId":"35c96520139253fe0a0a1ca1f3001cacbe9e38b6b594a4ab17c61519a4148932","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T16:41:40.265Z"}
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{"targetType":"network","priorAlternateCount":0,"nativeThinking":"Never","nativeRawData":"Very inaccurate","otherPriors":["tabular","spatial","grouped"],"datasetLabel":"Neuroinformatics Data","datasetSubmitTimestamp":"2019-10-25T16:47:53.372Z","terminology":{},"alternateDefinitions":{},"nodeClassCount":"1","edgeClassCount":"1","edgeDirection":"Undirected","exampleNetwork":{"nodes":[{"label":"Neuron"}],"edges":[{"source":0,"target":0,"directed":false}]},"networkDetails":[],"hardToImagine":"Agree","newQuestions":"Agree","inaccurate":"Neither agree nor disagree","useful":"Neither agree nor disagree","moreLikely":"Disagree","needsNewData":"Disagree","planToReshape":"Disagree","hardInPractice":"Neither agree nor disagree","softwareList":"Python, c++","startTimestamp":"2019-10-25T16:50:14.084Z","browserId":"72134ddf86833ec3c8708d64dcaec195df000b130693fa9913fb86cf8b0e3593","surveyVersion":"1.0.0","submitTimestamp":"2019-10-25T16:50:14.085Z"}
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