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46
datasets available to search
ShareScore release 0.9.0
Dataset results
46 results for “Data Scientists”
Data from CoFish: co-designing citizen science between fishers and scientists to monitor the phosphorus distribution across two Lake Geneva basins
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Data from: Continent‐scale phenotype mapping using citizen scientists’ photographs
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Data from: Data sharing, management, use, and reuse: practices and perceptions of scientists worldwide
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Juxtaposition of climate change contrarians and scientists in the media using Media Cloud data
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Good scientists share data?
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and who owns research data. Our interview guest will be Dr Daniel Barron from Yale University, who wrote an article on these issues in Scientific American. We will cover the issue of what the priorities for research data are, the Jack Gallant controversy, and should Open Science principles be enforced.</p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://medicine.yale.edu/psychiatry/nrtp/residents/daniel_s_barron.profile">Daniel Barron</a> <ul> <li><a href="http://danielsbarron.com/">Daniel Barron Articles</a></li> <li><a href="https://twitter.com/daniel__barron">Daniel Barron Twitter</a></li> </ul> </li> <li><a href="https://blogs.scientificamerican.com/observations/how-freely-should-scientists-share-their-data/">Barron’s Scientific American article</a></li> <li><a href="https://www.nature.com/articles/s41467-018-05227-z">Nature article: Data sharing and the future of science</a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“Who owns research data?”</p>
Data from: Research culture: a survey of travel behaviour among scientists in Germany and the potential for change
Awareness of the environmental impact of conferences is growing within the scientific community. Here we report the results of a survey in which scientists in Germany were asked about their attendance at conferences, their reasons for attending, and their willingness to explore new approaches that would reduce the impact of conferences on the environment. A majority of respondents were keen to reduce their own carbon footprint and were willing to explore alternatives to the traditional conference.
Multiverse Notebook: Shifting Data Scientists to Time Travelers (Supplemental Material)
<p>The collected revisions and the results of our manual inspectionspresented in "Multiverse Notebook: Shifting Data Scientist to Time Traveler."</p>
Data supporting: Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists
<p>This dataset contains all data collected by citizen scientists in support of the publication: "Hansen N, Bryant A, McCormack R, Johnson H, Lindsay T, Stelck K, et al. (2021) Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists. PLoS ONE 16(12): e0261817. https://doi.org/10.1371/journal.pone.0261817".</p> <p>This study evaluates the performance of several commercially available nonfouling polymers using citizen science, to identify the best performing chemistry for future applications as bacteria resistant coatings.<b> </b>The specific polymer chemistries tested were zwitterionic sulfobetaine methacrylate (SBMA), and polyampholytes composed of [2-(acryloyloxy)ethyl] trimethylammonium chloride and 2-carboxyethyl acrylate (TMA:CAA) or TMA and 3-sulfopropyl methacrylate (TMA:SA). Each polymer chemistry is known to exhibit bacteria resistance, and this study utilizes a citizen science approach to compare the performance of these chemistries.</p>
Scientists attitudes toward sharing data over time
<p>This data is derived from three previous surveys performed by members of DataONE to gauge scientists' attitudes and practices around data sharing and data management, and also to determine their satisfaction with the resources provided to them for sharing and data management. The data and analysis here are a subset of the questions from those three surveys. A subset of questions from each of the previous surveys was used to assess changes in attitudes towards sharing data and data management over time. The data are also available on <a href="https://github.com/olendorf/scientist_surveys/">Github</a>. Consult the README contained in the ZIP file for usage and other notes.</p>
Bibliometric data ESS scientists up until 2020
<p>Bibliometric data for 126 of a BSOs’ staff has been collected including all publications from the beginning of their research careers. The dataset is shaped by author, where the variables are represented as totals.</p>
Data supporting: Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists
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Data from: Ocean research priorities: similarities and differences among scientists, policymakers, and fishermen in the United States
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Data from: Research culture: a survey of travel behaviour among scientists in Germany and the potential for change
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Data from: How scientists perceive the evolutionary origin of human traits: results of a survey study
Various hypotheses have been proposed for why the traits distinguishing humans from other primates originally evolved, and any given trait may have been explained both as an adaptation to different environments and as a result of demands from social organization or sexual selection. To find out how popular the different explanations are among scientists, we carried out an online survey among authors of recent scientific papers in journals covering relevant fields of science (palaeoanthropology, palaeontology, ecology, evolution, human biology). Some of the hypotheses were clearly more popular among the 1266 respondents than others, but none was universally accepted or rejected. Even the most popular of the hypotheses were assessed "very likely" by <50 % of the respondents, but many traits had 1–3 hypotheses that were found at least moderately likely by >70 % of the respondents. An ordination of the hypotheses identified two strong gradients. Along one gradient, the hypotheses were sorted by their popularity, measured by the average credibility score given by the respondents. The second gradient separated all hypotheses postulating adaptation to swimming or diving into their own group. The average credibility scores given for different subgroups of the hypotheses were not related to respondent's age or number of publications authored. However, (palaeo)anthropologists were more critical of all hypotheses, and much more critical of the water-related ones, than were respondents representing other fields of expertise. Although most respondents did not find the water-related hypotheses likely, only a small minority found them unscientific. The most popular hypotheses were based on inherent drivers, i.e. they assumed the evolution of a trait to have been triggered by the prior emergence of another human-specific behavioural or morphological trait, but opinions differed as to which of the traits came first.
Data from: Can observation skills of citizen scientists be estimated using species accumulation curves?
Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project's tasks. To improve the quality of a citizen science project's outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.
Dataset for Data Analysis and Visualization with Python for Social Scientists lesson
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Oportunitats Laborals a LinkedIn: Dataset de Compatibilitat d'Habilitats i Requisits. 500 feines de Data Scientist
<p><strong>El conjunt de dades recull informació detallada sobre les ofertes de treball trobades a LinkedIn per un títol de treball específic. Cada fila representa una oferta laboral i inclou camps com el nom de l’empresa, el títol de la posició, la ubicació, la data de publicació, el nombre de sol·licituds, i la modalitat de treball (presencial/remot). A més, el dataset aporta una visió sobre els requisits exigits per la posició i la compatibilitat de l'usuari amb aquests requisits, indicant quins compleix i quins no, així com el nombre total d'habilitats requerides. Aquestes dades poden ser útils per a persones que busquen feina i volen identificar ràpidament les oportunitats més adequades, ja que permeten avaluar quines posicions s'alineen millor amb les seves habilitats actuals i veure en quins requisits podrien necessitar millorar o aprofundir.</strong></p>
Towards an ELSA Curriculum for Data Scientists – A first approach
<p>Presentation of the general concept and the proposed content of an ELSA Curriculum for Data Scientists; we also will discuss implementation issues of the curriculum application such as program duration, means of content delivery, and evaluation methods.</p>
Data from: Changes in data sharing and data reuse practices and perceptions among scientists worldwide
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Data from: Can observation skills of citizen scientists be estimated using species accumulation curves?
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.