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29,897 results for “activism”
Accelerated Mechanophore Activation and Drug Release in Network Core-Structured Star Polymers Using High-Intensity Focused Ultrasound
<div>Data of the associated manuscript and supporting information sorted after Figures, Schemes, and Tables.</div>
Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS
<p>A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O).<br>Citation of the article related to this dataset:</p> <div> <div><strong>Guevara, E.</strong> (2024). <em>Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS</em> [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>
Model results: Model-based decision support for the choice of active spring frost protection measures in apple production
<p><strong>Background: </strong></p> <p>Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters.</p> <p>As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region).</p> <p><strong>Repository content:</strong></p> <p>This repository contains the simulation results of 100,000 Monte Carlo runs with the model.</p> <p>The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.</p>
Replication data for Global variation in the preferred temperature for recreational outdoor activity
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2024): <a href="https://doi.org/10.1016/j.jeem.2024.103032">Global variation in the preferred temperature for recreational outdoor activity</a>, published in the Journal of Environmental Economics and Management.</p> <p>The main data on temperature and rainfall are from ERA5 reanalysis (Hersbach et al. 2018). Data on mobile phone activity are from the Google Mobility Reports. Data on GDP per capita are from the World Bank.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Bibliography</strong></p> <ul> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47</li> </ul>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: insertion data and derivative models
<p>This data repository provides access to the LINE-1 (L1) insertion site data from Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_actual_and_random_insertions_help.docx</strong> for more detailed information.</p> <p>Files <strong>weighted_model_142_102917_corrected.txt</strong> and <strong>weighted_model_142_102917_uncorrected.txt</strong> carry a list of all possible 7-mer insertion sites, one per row, with site weights calculated based on observed L1 insertion sites. The files are either corrected or uncorrected for the frequency of those sites as found in the human genome, respectively. A header line defines the columns.</p> <p>The weight files described above were used to construct the simulated <strong>hg19 </strong>insertions sets described below.</p> <p>Archive <strong>all_insertion_sets.tar</strong> contains a series of insertion files, each with the complete data set used to analyze insertions from the named cell line.</p> <p>Column 5 is the iteration number, where:</p> <ul> <li>iteration == 0 identifies the actual, observed insertions</li> <li>each iteration > 0 identifies one round of simulation, up to 10K total simulations</li> <li>each iteration has the same number of insertions</li> </ul> <p>Column 4 is the insertion number within each actual or simulated insertion set.</p> <p>Please see the companion Zenodo data set:<br>10.5281/zenodo.12538130<br>for more information about creating insertion site models from your own insertion data.</p>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: creating new models
<p>This data repository provides code and general instructions for creating new LINE-1 (L1) insertion site models as described in Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_Simulation_Files_help.docx</strong> for more detailed information.</p> <p>File <strong>L1Simulation_filelist.xlsx</strong> lists the files described in the Word file and found in <strong>L1Simulations.tar.gz</strong>.</p> <p>Required large file <strong>ChrmsALL_table_sortEN.bed.gz</strong> is found individually. Importantly, like the paper analysis, this file is specific to the <strong>hg19 </strong>version of the human genome.</p> <p>Please see the companion Zenodo data set<br>10.5281/zenodo.8233858<br>if you only want to access our insertion data and the resulting models we created.</p> <p> </p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
NEC Year 3 Activities Infographic
<p>Infographic showing Year 3 activities and accomplishments of the NNLM National Evaluation Center. </p> <p>It depicts highlights of some of the work carried out by the entire NEC Team.</p> <p>This resource is supported by the National Library of Medicine of the National Institutes of Health under Award Number U24LM013751<strong><span>.</span></strong> The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
Fishing activities and trajectories for 2 fishing vessels
<p>This data set provides the pseudo-positions in space and time of two fishing vessels and the associated activities (fishing, cruising, stopped, recorded by an on board observer). It supports the analyses provided in a paper published in Methods in Ecology and Evolution and the methods of the R package m2b (https://cran.r-project.org/package=m2b). For privacy concerns, original latitude, longitude, time and vessels id were modified. Spatial data were scaled and centred to a fictional position (R'lyeh position, Lovecraft 1928) keeping the relative geometry unchanged (acceleration, time between two positions....). Time and vessel id were modified in the same manner, keeping the relative properties of the tracks unchanged (time succession, different vessel id...). Vessel id and time are purely fictional and follow the historical context proposed by Lovecraft (1928) in the R'lyeh surroundings. Again, if the absolute spatial and temporal description of the fishing track were changed, their relative mathematical properties are conserved and can support behaviour detection based on relative movement analysis.</p> <p> </p> <p>For the data_vessel.csv file (csv file with header), the variables are</p> <p>x : pseudo longitude</p> <p>y : pseudo latitude</p> <p>t : pseudo time in year-month-day hour:minutes:second format</p> <p>b: fishing activity, namely "fishing", "cruising", "stopped"</p> <p>id: unique id by vessels (fictional names).</p> <p><br> Reference</p> <p>H. P. Lovecraft, "The Call of Cthulhu" (1928)</p> <p> </p>
Dataset - Gravisensors in plant cells behave like an active granular liquid
<p>Dataset corresponding to data shown in article : https://hal.archives-ouvertes.fr/hal-01425298v2 (avalanches of statoliths pile in wheat coleoptile cells, avalanches of silica micro-particles in biomimetic cells, vertical fluctuations of statoliths in wheat coleoptile cells or extracted from their cells).</p> <p>Examples of python scripts that open and plot the data are also included.</p>
WSSSPE5.1 presentations: Research software sustainability activity analysis
<p>Distribution of actors, actions and actees from <a href="https://www.slideshare.net/danielskatz/research-software-sustainability-wssspe-urssi">Daniel S. Katz' schematic of research software sustainability</a> over presentations given at the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1) on 6 September 2017 in Manchester, UK (Proceedings: <a href="https://doi.org/10.6084/m9.figshare.c.3869782.v3">https://doi.org/10.6084/m9.figshare.c.3869782.v3</a>).</p>
(No) Influence of Continuous Integration on the Development Activity in GitHub Projects — Dataset
<p>This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.</p> <p>We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:</p> <ol> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),</li> <li>were active for at least one year (365 days) before the first build with Travis CI (before_ci),</li> <li>used Travis CI at least for one year (during_ci),</li> <li>had commit or merge activity on the default branch in both of these phases, and</li> <li>used the default branch to trigger builds.</li> </ol> <p>To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.</p> <p>We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).</p> <p>We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:</p> <ol> <li>have Java or Ruby as their project language</li> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)</li> <li>have commit activity for at least two years (730 days)</li> <li>are engineered software projects (at least 10 watchers)</li> <li>were not in the TravisTorrent dataset</li> </ol> <p>In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.</p> <p>This dataset contains the following files:</p> <p><strong>tr_projects_sample_filtered_2.csv</strong><br> A CSV file with information about the 113 selected projects.</p> <p><strong>tr_sample_commits_default_branch_before_ci.csv<br> tr_sample_commits_default_branch_during_ci.csv</strong><br> One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The branch to which the commit was made.<br> hash_value: The SHA1 hash value of the commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.</p> <p><strong>tr_sample_merges_default_branch_before_ci.csv<br> tr_sample_merges_default_branch_during_ci.csv</strong><br> One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The destination branch of the merge.<br> hash_value: The SHA1 hash value of the merge commit.<br> merged_commits: Unique hash value prefixes of the commits merged with this commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.<br> pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message).<br> source_user: GitHub login name of the user who initiated the pull request (extracted from log message).<br> source_branch : Source branch of the pull request (extracted from log message).</p> <p><strong>comparison_project_sample_800.csv</strong><br> A CSV file with information about the 800 projects in the comparison sample.</p> <p><strong>commits_default_branch_before_mid.csv<br> commits_default_branch_after_mid.csv</strong><br> One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.</p> <p><strong>merges_default_branch_before_mid.csv<br> merges_default_branch_after_mid.csv</strong><br> One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Longitudinal Survey Data: Wave 1 & Wave 2: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ longitudinal survey that was carried out in Estonia from October 2016 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by Univversity of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Survey Data: Estonia: Wave 2
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from November 2017 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by University of Taartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Pilot Questionnaires: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from May to June 2016. It merges results of two polls (16-18 and 20-26 year olds). Survey was conducted by University of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
ACTIV-ES: a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions
<p><strong>DESCRIPTION</strong>: ACTIV-ES is a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions. Titles for each of these three countries were seeded from the Internet Movie Database, subtitle data for the hearing impaired was provided by Opensubtitles.org and was post-processed to correct/remove subtitle, OCR and diacritic artifacts and annotated for part-of-speech.</p> <p>The data is available in two main formats: 1) running text for each document and 2) 1:5 gram aggregate files. Each format includes a plain text and part-of-speech annotated version. Document names reflect the language code, country, year, title, type, genre (first genre listed in the IMDb), and IMDb ID.</p> <p>For more information about the development and evaluation of these resources and to cite this work refer to:</p> <p>Francom, J., Hulden, M. and Ussishkin, A.. (2014) ACTIV-ES: a comparable, cross-dialect corpus of 'everyday' Spanish from Argentina, Mexico, and Spain. In Proceedings of the Ninth Annual Language Resources and Evaluation Conference, Reykjavik, Iceland. European Language Resources Association (ELRA).</p> <p>In <strong>version .02</strong> of the tagged running format corpus in the /eagles directory has been added which includes the EAGLES tagset. This tagset is much more fleshed out than the simplified tagset in the /tagged directory. For information on the tagset refer here: <a href="http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html">http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html</a>.</p>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
A Panel Data Set of Cryptocurrency Development Activity on GitHub
<p>Contents:</p> <ul> <li><strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all data, sorted by date. This file contains some imputed values for missing data, and all fields across all repositories and normalized to "null". This is the most convenient form to use.</li> <li><strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all, sorted by date. It is the raw data after processing the raw format.</li> <li><strong>raw-data-2018-01-21-to-2019-02-04.tar.gz</strong>: The raw format of data collected (S-expressions). Contains additional contributor data and CoinMarketCap data not currently in the CSV datasets.</li> <li><strong>recovered.patch</strong>: The modification on <strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong> after recovering (imputing) data<strong>, </strong>showing what was recovered.</li> <li><strong>recovered-normalized.patch</strong>: The modification of <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>after normalizing the recovered data set. Thus, patching <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>with<strong> recovered.patch</strong>, then <strong>recovered-normalized.patch </strong>gives <strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong></li> <li><strong>missing-dates.txt</strong>: Days for which we missed GitHub data collection (partial or completely).</li> </ul> <p>Related publications:</p> <pre><code>@inproceedings{van-tonder-crypto-oss-2019, title = {{A Panel Data Set of Cryptocurrency Development Activity on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "{van~Tonder}, Rijnard and Trockman, Asher and {Le~Goues}, Claire", series = {MSR '19}, year = 2019 } @inproceedings{trockman-striking-gold-2019, title = {{Striking Gold in Software Repositories? An Econometric Study of Cryptocurrencies on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "Trockman, Asher and {van~Tonder}, Rijnard and Vasilescu, Bogdan", series = {MSR '19}, year = 2019 }</code></pre> <p>Related code: <a href="https://github.com/rvantonder/CryptOSS">https://github.com/rvantonder/CryptOSS</a></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.