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Non-probability sampling
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Collection date: 2015-10-01
Response options: 0, 1, 2, 4
Collection date: 2015-10-01
Response options: 0, 1, 2, 5, 8, 6
Collection date: 2015-10-01
Response options: 3, 1, 25, 2, 0, 5, 11
Collection date: 2015-10-01
Response options: 3, 1, 2, 0, 4
Collection date: 2015-10-01
Response options: city/surburb or town centre strip, regional shopping centre, stand-alone pharmacy, neighbourhood shopping centre, medical centre, other
Collection date: 2015-10-01
Response options: 12, 13, 14, 8, 11, 9.5, 11.25, 10, 10.5, 16, 9, 24, 11.5, 5, 15, 14.5, 8.5
Survey question
Collection date: 2023-01-01
Response options: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, White, Other, Prefer not to answer, 9999
Survey question
Collection date: 2023-01-01
Response options: Male, Female, Transgender male, Transgender female, Gender variant/non-conforming, Not listed, Prefer not to answer
Collection date: 2018-01-01
Response options: The hardest thing to understand about other people's data is often selection criteria that are hard to express., In my field incomplete capture and sample bias are inherent. Reserachers must use the proper design and estimator to minimize these biases. They must report capture/detection probabilities and how thesse were estimated along with the other metadata., ecology data is highly variable and small differences in collection have huge impacts on data meaning. it's a difficult thing to standardize!, License assignment were easy, clear, and respected, all data collection and processing by others was done with open source code that was distributed with the data., Associated cleaning/organization scripts were available, Clear information on how the data to be cited is available!, Conditions/licenses for reuse were explicit and there was documentation that data were collected in accordance with ethical standards., If data was integrated/federated across different domains where appropriate, Most of the data I have to manage are really very dirty and a mess. No metadata standard can do anything against that, and I wish I had a data dictionnary describing at least unit of measurement and types of the variables that are created in datasets. Most of the metadata standards do not even think to that issue., if I can vizualize the data and its documentation, I knew where it came from and for what purpose it was collected., there is sufficient identifiers methods, If there are questions about the data (for example, that did not seem important at the time), I can ask the originating lab directly about their methods., I did not reuse data, so I would rather have answered "N/A" when available, I use data as they are provided on the microscopic slides, For some fields (i.e. experimental chemistry) the "data sets" are rather small, and none of the questions are really applicable. To elaborate: it takes *a lot* of effort to obtain what amounts to a single data point, and that single result is extremely meaningful., I am not sure, The paper the data were published in also published their data processing tools (i.e. R or python scripts)., a full record on how other use the data.
Collection date: 2018-01-01
Response options: Poorly worded question - all of my data are store on an institutional serve which is also a national center., Classified data, Confidentiality - some of the older data did not collect consent to archive and we cannot get retrospective consent. University will not grant ethical approval to share despite anonymisation and access controls on archvied data., It is available to others, My data are shared with cooperating agencies so I don't have full authority to make them available. I am also fundamentally opposed to making research data publicly available., unsure of rights in the case of qualitative data interviews, A large portion of the data that I analyze are compiled from other public sources., Data is already public; I am re-using., The datasets are not completed yet., Not well organized, Relevant models/examples for social science data sharing are few and far between, extremely limited in methods & discipline scope, and are not configured well for the particulars of human subjects data considerations. Not to mention I've never actually heard of qualitative human subjects data being reused., I work with very large files (LOTS of 3D reconstructions from x-ray tomography)., Intellectual Property, Some data are incomplete or do not fit in available repository formats, Fees required, The type of data I work with (qualitative archival data) doesn't really lend itself to me sharing it; however, it is available from the same sources from which I accessed it. Other qualitative data I use is often restricted from sharing due to human subjects guidelines., all data are available, Found some people use different way to get other's data and publish with other's credit and permission, There is always a place to make data public, there are no excuses; but in some cases the data just does not belong only to us, but to collaborators and not all are willing to put all publicly and soon, I'm working hard now to make most of my and my partner's data available - its been a slow process - and expensive