Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
46
datasets available to search
ShareScore release 0.9.0
Dataset results
46 results for “user survey”
bioRxiv and medRxiv 2023 user survey
<p>This dataset presents the anonymized results of the 2023 bioRxiv and medRxiv user survey, which received responses from 7135 participants. Conducted between October 25th and December 19th, 2023, the survey aimed to understand community usage of the preprint servers, with a focus on improving service delivery and functionality. To protect respondent anonymity, all free-text entries have been removed from the dataset. </p>
Miraheze User Survey 2017
<p>Miraheze[1]'s 2017 user survey data.</p> <p>Data gathered by the survey link displayed on every visit. Questions are included in the file.</p> <p> </p> <p>[1]: https://meta.miraheze.org</p>
PIE News Early User Survey data
<p>The PIE News “Early User Survey” is an evaluation-related activity which is grounded on the adoption of a subjective understanding of the term “precarious”, as a condition and not as a contractual status. This means that one can be precarious also with a permanent work contract, because the many possible dimensions of uncertainty (e.g.: employer under risk, crisis of the job market) drive an objective or just perceived individual precarious feeling. The more objective understanding of the precarious experience will probably be a project outcome.</p> <p>The survey ended the 25 October 2016 involving people contacted by the PIE News pilot leaders in Italy, Croatia and the Netherlands.</p>
Yavaa - User Survey Results
<p>The raw responses excluding personal data, submitted files, and derived attributes from the user survey of Yavaa conducted as part of the PhD thesis.</p>
Dataset: Surveys (Tourists, Locals, Users). St. Georges Cultural Quarter (Leicester) & Ouseburn Valley (Newcastle upon Tyne). United Kingdom
<p>Dataset of 6 survey processes applied to different populations –locals, tourists and users– from St. George´s Cultural Quarter (Leicester) and the Ouseburn Valley (Newcastle Upon Tyne) between September 2016 and April 2020.</p> <p>Dataset with the answers to a series of questionnaires carried out to determine the brand awareness of the cultural and creative districts of Leicester, the St. George's Quarter, and Newcastle Upon Tyne, the Ouseburn Valley.</p> <p>In total, six survey processes have been carried out in which two different questionnaires have been used. The first of them is aimed at tourists and locals from Leicester and Newcastle Upon Tyne and, the second, at users of their districts St. George's Quarter and the Ouseburn Valley.</p> <p>The first questionnaire aims to determine the notoriety of the districts among tourists and locals. Hence two different survey processes were carried out using the same questionnaire, one among tourists and the other among locals. The questionnaire contains a total of 16 questions, some of them open and others closed, and is made up of five blocks intended to analyze:</p> <ol> <li>Sociodemographic characteristics. Through the first two questions of the questionnaire, the aim is to obtain sociodemographic data of the sample surveyed, such as age and sex.</li> <li>Degree of knowledge of the concept of cultural and creative districts in the city of Leicester/Newcastle Upon Tyne. This section aims to study the degree of spontaneous and suggested notoriety of the district and its logo among the public, whether tourists or locals. It asks, directly and indirectly, about some cultural and creative districts in the city and their corporate visual identity. First, it is done indirectly (spontaneous awareness) and then directly and concretely (suggested awareness).</li> <li>Degree of knowledge of different institutions, companies or organizations in the district. This part aims to detect whether the local public knows the most relevant actors in the district, regardless of whether or not the respondent is aware of the name given to the space. For this, participants are asked about the most representative areas of each district and other general activities that they may know about.</li> <li>Motivation to attend, know, use and visit the district regularly. This block aims to determine the reasons why the respondents make use of a cultural and creative district, as well as the frequency with which they visit it.</li> <li>Opinions, variables, values and characteristics linked to cultural and creative districts in general. The respondents are asked about the importance that these spaces in the city have for them and why.</li> </ol> <p>The questionnaire includes both mandatory questions and other optional ones, considering that those people who were unaware of a cultural and creative district or certain spaces within it could not answer some of the questions. It is worth noting some details of each survey process:</p> <ul> <li>The first survey process was carried out in person in September 2016 among tourists and visitors in Leicester, in the city centre, at the Leicester Tourist Office located at Gallowtree Gate.</li> <li>The second survey process took place between September and December 2017 among the inhabitants of Leicester. It was carried out in person in the city centre (specifically at Haymarket Memorial Clock Tower, Humberstone Gate and at the De Montfort University campus) and in the surroundings of Leicester's cultural and creative district (in Rutland Street).</li> <li>The third and fourth survey processes were carried out between September and December 2018 among the inhabitants and tourists of Newcastle Upon Tyne, respectively. Both processes were carried out in person in the city centre (specifically in Eldon Square, Grey's Monument, Northumberland Street and Newgate Street).</li> </ul> <p>During all these processes, the necessary instructions were given to the respondents so that they could answer the questionnaire correctly, in person and orally, and the answers obtained were recorded on a digital tablet.</p> <p>In Leicester, 50 surveys were carried out among tourists and 306 among locals. On the other hand, in Newcastle Upon Tyne, 62 surveys were carried out among tourists and 60 among locals. All of these surveys were carried out in person.</p> <p>In addition, another survey process different from the ones described above was carried out, for which a second questionnaire was used. This questionnaire has a similar structure to the first and many of the blocks are common, but focuses on understanding the reasons that led current users to a cultural and creative district become such, and how they make use of the district and its logo, its corporate visual identity, its nature as a cultural and creative space, the existing information about it, etc. The main objective of this second questionnaire, beyond determining the suggested notoriety of the districts of St. George's Quarter and the Ouseburn Valley among its users, has focused on studying the phenomenology described. To do this, a total of 17 questions are combined, some of them open and others closed, distributed into five blocks intended to analyse:</p> <ol> <li>Sociodemographic characteristics. Through the first three questions of the questionnaire, the aim is to obtain sociodemographic data of the sample surveyed, such as age, gender and current employment status.</li> <li>Opinions, variables, values and characteristics linked to cultural and creative districts in general. Respondents are asked about the importance of these spaces in the city and why they are important (if respondents consider they are).</li> <li>Degree of awareness of the nature of the cultural and creative districts among their users. This section aims to study the degree of suggested notoriety of a district and its logo among users. It asks directly about the identification of the space as a cultural and creative district and the knowledge (or not) of its corporate visual identity.</li> <li>Degree of knowledge and use of the different institutions, companies, organizations and actors in the district. This part aims to detect the level of knowledge and frequency of use of the most relevant spaces in a district by the surveyed users. Respondents are asked about the most representative areas of each district and other general activities that they may know about in them.</li> <li>Motivation to attend, get to know and visit the district regularly. This block aims to determine the reasons why respondents make use of a cultural and creative district, as well as the frequency with which they visit the space and with whom they do so. In addition, it focuses on studying how they get to it (public transport, walking, car,...) and through which means (social networks, traditional media, word of mouth,...) the surveyed users are aware of the different events that take place in the corresponding district.</li> </ol> <p>The questionnaire includes both mandatory questions and other optional ones, once again considering that those people who may be unaware of certain spaces in the cultural and creative district or certain features of it may not be able to answer some of the questions.</p> <p>Both survey processes were carried out online between March and April 2019 and 65 responses have been obtained among users of the St. George's Quarter and 86 among users of the Ouseburn Valley.</p> <p>During all the survey processes, the necessary instructions were given to the respondents so that they could answer the questionnaire correctly. Likewise, they were informed of the nature of the investigation and the identity of the interviewer, and also were provided with a contact email to send any questions or suggestions.</p> <p>In this case, the questionnaire was prepared using Google Forms and distributed from March 1 to April 30, 2020 "online" through "e-mailing" and Social Networks such as Google +, Facebook, Twitter and Instagram. For this, the different workers in the areas of interest were identified thanks to the web directories available on the corporate pages of each cultural, creative, educational actor, etc. and were contacted. In addition, concerning social networks, the questionnaire was distributed using various specific interest groups existing in the districts and through the corporate accounts of the actors in the districts, which facilitated the distribution of the questionnaire on their profiles on social networks.</p>
User Requirements and Evaluation Survey
<p>This is the survey used to elicitate the user requirements.</p> <p>Also, the results are included.</p>
Supplementary material for the ITP'23 article "Lessons for Interactive Theorem Proving Researchers from a Survey of Coq Users"
<p>This artifact contains the supplementary files for the ITP'23 article "Lessons for Interactive Theorem Proving Researchers from a Survey of Coq Users". More specifically, it contains:</p> <ul> <li>the Limesurvey structure exported file (<code>Limesurvey/survey-structure.lss</code>);</li> <li>the HTML print of the survey in English and Chinese (<code>Limesurvey/questionnaire_english.html</code> and <code>Limesurvey/questionnaire_chinese.html</code>);</li> <li>the Jupyter notebook (<code>Coq-survey-analysis.ipynb</code>) and the Stata code (<code>regressions/Regressions_and_Romano-Wolf.do</code>) that were used to produce the results;</li> <li>the plots for the answers to all the closed questions, as well as plots for some interactions between answers to multiple closed questions in <code>png</code> and <code>svg</code> formats (<code>assets/</code>);</li> <li>manual analysis of some open text questions (<code>coded_answers/</code>);</li> <li>answers to open text questions (<code>open_answers/</code>).</li> </ul> <p>This artifact does <em>not</em> contain the full raw data from the survey. These data have been deleted, following the GDPR compliance statement that was displayed at the beginning of the survey. The open text answers that are made available through this artifact have been sanitized to remove any personally identifiable element.</p> <p>File listing</p> <ul> <li>README.md: this README</li> <li>Limesurvey <ul> <li>questionnaire_english.html: survey HTML print in English</li> <li>questionnaire_chinese.html: survey HTML print in Chinese</li> <li>survey-structure.lss: Limesurvey structure export</li> </ul> </li> <li>Coq-survey-analysis.ipynb: Jupyter notebook used to produce plots</li> <li>regressions <ul> <li>Regressions_and_Romano-Wolf.do: Stata code used to do the regressions appearing in the article</li> </ul> </li> <li>assets <ul> <li>many <code>png</code> and <code>svg</code> files for plots showing quantitative results</li> </ul> </li> <li>coded_answers <ul> <li>renaming.md: manual analysis of the answers to the open text question "If you wish to elaborate on why Coq should / should not be renamed, feel free to do it here."</li> <li>renaming_choices.md: manual analysis of the answers to the open text question "If you wish to share any specific arguments in favor or against some specific name choices, please do so here."</li> <li>contributing_experience.csv: answers to the open text question "Feel free to elaborate on the contributing experience, what we can do better, or why you do not contribute." with manual analysis</li> <li>doc_improvements-grouped.docx manual analysis of the answers to the open text question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important?" (in the context of a question on "How important are improvements to the following aspects of the Coq documentation?")</li> </ul> </li> <li>open_answers: Each table has been reordered and has a different indexing, so relating answers from different tables is not possible. Furthermore, answers have been checked and sanitized to remove any personally identifiable elements. <ul> <li>ci_feedback.csv: answers to the question "If you have general feedback on CI in the Coq ecosystem, feel free to share it here." Also shared at: <a href="https://github.com/coq-community/manifesto/issues/141">https://github.com/coq-community/manifesto/issues/141</a></li> <li>contributing_experience.csv: answers to the question "Feel free to elaborate on the contributing experience, what we can do better, or why you do not contribute."</li> <li>coqide_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in CoqIDE?" Also shared at: <a href="https://github.com/coq/coq/issues/16580">https://github.com/coq/coq/issues/16580</a></li> <li>coq_improvements.csv: answers to the question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important? Also, feel free to tell us how Coq does compared to other proof assistants you have experience with." (in the context of a question on "In order to make you more productive in Coq and to encourage others to learn and use Coq, how important are improvements in the following areas?")</li> <li>coqtail_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in Coqtail?" Also shared at: <a href="https://github.com/whonore/Coqtail/issues/277">https://github.com/whonore/Coqtail/issues/277</a></li> <li>distracting_company_coq_features.csv</li> <li>doc_improvements.csv: answers to the question "Feel free to elaborate on any of the items listed above, their importance, etc. Are there other improvements that you think would be important?" (in the context of a question on "How important are improvements to the following aspects of the Coq documentation?")</li> <li>extraction_targets.csv: answers to the question "If you're interested in new extraction targets, which languages do you want?" Also analyzed quantitatively in: assets/extraction-targets-barplot.png</li> <li>jscoq_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in jsCoq?" Also shared at: <a href="https://github.com/jscoq/jscoq/issues/261">https://github.com/jscoq/jscoq/issues/261</a></li> <li>jupyter_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in coq_jupyter?" Also shared at: <a href="https://github.com/EugeneLoy/coq_jupyter/issues/46">https://github.com/EugeneLoy/coq_jupyter/issues/46</a></li> <li>jupyter_support.csv: answers to the question "Have you had any issues or lack of support for coq_kernel for any service? If so, feel free to share here." Also shared at: <a href="https://github.com/EugeneLoy/coq_jupyter/issues/46">https://github.com/EugeneLoy/coq_jupyter/issues/46</a></li> <li>languages.csv: answers to the question "What languages would be the most useful to support?" Also analyzed quantitatively in: assets/languages-barplot.png</li> <li>learning_experience.csv: answers to the question "How was your experience while learning Coq? For example, what were the easiest and/or most difficult parts of the process? Do you have suggestions to improve the experience?"</li> <li>proof_general_customizations.csv: answers to the question "Do you use specific customizations or fixups of Proof General or Company-Coq (in your ~/.emacs)? If yes, briefly speaking, what are these customizations and would you like to have some of them applied by default?" Also shared at: <a href="https://github.com/ProofGeneral/PG/issues/671">https://github.com/ProofGeneral/PG/issues/671</a></li> <li>proof_general_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in Proof General?" Also shared at: <a href="https://github.com/ProofGeneral/PG/issues/671">https://github.com/ProofGeneral/PG/issues/671</a></li> <li>renaming.csv: answers to the question "If you wish to elaborate on why Coq should / should not be renamed, feel free to do it here."</li> <li>renaming_choices.csv: answers to the question "If you wish to share any specific arguments in favor or against some specific name choices, please do so here."</li> <li>survey_issues.csv: answers to the question "Did you encounter any issues with the survey that you'd like to report or do you have other feedback that we should hear about?"</li> <li>vim_compatibility.csv: answers to the question "What Vim / NeoVim features or plugins would you like to have better integrated with Coqtail? " Also shared at: <a href="https://github.com/whonore/Coqtail/issues/277">https://github.com/whonore/Coqtail/issues/277</a></li> <li>vscoq_improvements.csv: answers to the question "What improvements, bug fixes and new features would you most like to see in VsCoq?" Also shared at: <a href="https://github.com/coq-community/vscoq/issues/308">https://github.com/coq-community/vscoq/issues/308</a></li> </ul> </li> </ul>
Users' privacy awareness survey (2019-2020), total results
<p><strong>In this survey, we inspected how aware people were of their online privacy and calculated their privacy awareness scores.</strong></p> <p><br> </p> <p><strong>privacy_awareness_questionnaire.pdf</strong></p> <p><strong> It is the Privacy Awareness survey questionnaire.</strong></p> <p> </p> <p><strong>calculating_privacy_awareness_scores_method.pdf</strong></p> <p><strong> It describes the method of calculating privacy awareness scores.</strong></p> <p> </p> <p><strong>raw_data.csv</strong></p> <p><strong> It contains the raw data set collected from the respondents. The first row contains the labels of questions from 1 to 15.</strong></p> <p> </p> <p><strong>filtered_out_data.csv</strong></p> <p><strong> It contains filtered-out data set and the calculated awareness scores. The first row contains the labels of questions from 1 to 15 and the awareness score. We applied the following erasure conditions on the raw data set:</strong></p> <p><strong> Q2 = A2 AND Q3 = A1</strong></p> <p><strong> Q2 = A2 AND Q3 = A2</strong></p> <p><strong> Q6 = A1 AND Q7 = A2</strong></p> <p><strong> Q6 = A1 AND Q9 = A2</strong></p> <p> </p>
Data set supplementing "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study"
<p>This is the data set used to conduct the analyses in the article published by the Journal of Medical Internet Research under the title "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study" (<a href="https://doi.org/10.2196/46231">https://doi.org/10.2196/46231</a>)</p> <p>This data set contains information collected of 1,084 respondents about their awareness, use, and usefulness of symptom checkers and several individual characteristics. </p>
L3Pilot Global User Acceptance Survey, First Phase Data
<p>The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the first phase of the survey with responses collected from 17 countries on five continents.</p> <p>Description.pdf contains information about the survey methodology and coding of the variables. For further information about the survey, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’.</p> <p>If you use the dataset, please cite it as: L3Pilot (2021). L3Pilot Global User Acceptance Survey, First Phase Data. https://doi.org/10.5281/zenodo.5255949</p> <p>For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a></p>
Quit attempts amongst tobacco users identified in the Tamil Nadu Tobacco Survey of 2015-16: A 3 year follow-up mixed methods study
<p><span><b>Objectives</b></span></p> <p><span>To determine current tobacco use in 2018-19, quit attempts made and to explore the enablers and barriers in quitting tobacco among tobacco users identified in the Tamil Nadu Tobacco Survey (TNTS) in 2015-16. </span></p> <p><span><span><b>Setting</b></span></span></p> <p><span><span>TNTS was conducted in 2015-16 throughout the state of TN in India covering 111363 individuals. Tobacco prevalence was found to be 5.2% (n=5208) </span></span></p> <p><span><span><b>Participants</b></span></span></p> <p><span><span>All tobacco users in eleven districts of TN identified by TNTS (n=2909) were tracked after three years by telephone. In-depth interviews (n=26) were conducted in a sub-sample to understand the enablers and barriers in quitting. </span></span></p> <p><span><span><b>Primary and Secondary Outcomes</b></span></span></p> <p><span><span>Current tobacco use status, any quit attempt and successful quit rate were the primary outcomes, while barriers and enablers in quitting were considered as secondary outcomes.</span></span></p> <p><span><span><b>Results </b></span></span></p> <p><span><span>Among the 2909 tobacco users identified in TNTS 2015-16, only 724 (24.9%) could be contacted by telephone, of which 555 (76.7%) consented. Of those who consented, 210 (37.8%) were currently not using tobacco (i.e. successfully quit) and 337 (60.7%) continued to use any form of tobacco. Of current tobacco users, 115 (34.1%) never made any quit attempt and 193 (57.3.8%) have made any attempt to quit. Those using smoking form of tobacco products (aRR=1.2, 95% CI: 1.1-1.4) and exposure to smoke at home (aRR=1.2, 95% CI: 1.1-1.3) were found to be positively associated with continued tobacco use (failed or no quit attempt). Support from family and perceived health benefits are key enablers, while peer influence, high dependence and lack of professional help are some of the barriers to quitting. </span></span></p> <p><span><b>Conclusion</b></span></p> <p><span>Two-thirds of the tobacco users continue to use tobacco in the last 3 years. While tobacco users are well aware of the ill-effects of tobacco, various intrinsic and extrinsic factors play a major role as a facilitator and lack of the same act as a barrier to quit.</span></p>
Survey Appendix - User awareness about Meta's Data for Good
<p>This survey aims to explore user awareness regarding Meta's 'Data for Good' program in the context of the upcoming EU Data Act. The study seeks to understand how many users are informed about the sharing of their data for public purposes. </p>
RELAX ESEM 2021 DR User Study Survey Results Shared Document
<p>Dataset referenced in the ESEM 2021 paper titled "Study of the Utility of Text Classification Based SoftwareArchitecture Recovery Method RELAX for Maintenance"</p>
HCIV User Evaluation Survey Questions and Results
<p>Survey questions and anonymous data collected from a user evaluation of the Human Centric Issue Visualiser (HCIV).</p>
Quit attempts amongst tobacco users identified in the Tamil Nadu Tobacco Survey of 2015-16: A 3 year follow-up mixed methods study
Open the record for dataset details and reuse information.
DMPTuuli user survey_ raw data 2018
<p>This is the raw data from the DMPTuuli user survey 2018.</p>
VCC User Survey Results
Open the record for dataset details and reuse information.
Data from: Data sharing through an NIH central database repository: a cross-sectional survey of BioLINCC users
Open the record for dataset details and reuse information.
Supplemental Table: Video breakdown of 1,895 NeuroByte user satisfaction survey responses
<p>Objective <br> To determine whether NeuroBytes is a helpful e-Learning tool in neurology through usage, viewer type, estimated time and cost of development, and post-course survey responses. <br> <br> Background <br> A sustainable continuing professional development (CPD) system is vital in neurology due to the field's expanding therapeutic options and vulnerable patient populations. In an effort to offer concise, evidence based updates to a wide range of neurology professionals, the AAN launched NeuroBytes in 2018. NeuroBytes are brief (<5 min) videos that provide high-yield updates to AAN members. </p> <p>Methods <br> NeuroBytes was beta tested from August–December 2018 and launched for pilot circulation from January–April 2019. Usage was assessed by quantifying course enrollment and completion rates; feasibility by cost and time required to design and release a module; appeal by user satisfaction; and impact by self-reported change in practice. <br> <br> Results <br> A total of 5,130 NeuroBytes enrollments (1,026±551/month) occurred from January 11–May 28, 2019 with a median of 588 enrollments per module (interquartile range, 194-922) and 37% course completion. The majority of viewers were neurologists (54%), neurologists in training (26%), and students (8%). NeuroBytes took 59 hours to develop at an estimated $77.94/hour. Of the 1,895 users who completed the survey, 82% were "extremely" or "very likely" to recommend NeuroBytes to a colleague and 60% agreed that the depth of educational content was "just right." </p> <p>Conclusions <br> NeuroBytes is a user-friendly, easily accessible CPD product that delivers concise updates to a broad range of neurology practitioners and trainees. Future efforts will explore models where NeuroBytes combines with other CPD programs to impact quality of training and clinical practice. <br> </p>
Data on: The role of technical characteristics in blockchain adoption: survey data from German social media network users
<p><span>Blockchain has become a hyped emerging technology that is predicted to be heavily influential in all our lives. Yet, until now, it has failed to deliver most of its advertised benefits. To tackle this problem and provide an explanation for the missing wider success, this study focuses on the role of technology features in the adoption of blockchain. Thus, this research integrates the view on technological characteristics, represented by aspects of the mindfulness concept, with the sociological aspects influencing technology adoption decisions based on the widely used unified theory of acceptance and use of technology (UTAUT). The resulting research model is evaluated using the partial least squares structural equation modelling (PLS-SEM) estimation approach with German social media network. The findings indicate that only high-level knowledge of distinct technology features (uniqueness) is influencing adoption decisions while the missing deeper understanding of these features hinders a careful evaluation of its benefits and meaningful use. This research expands the technology adoption literature by highlighting the role of technical characteristics and combining social, psychological and technological factors into one model. Further, it helps practitioners to understand the causes for the limited success of blockchain and advances the general knowledge on technology adoption.</span></p>
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.