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47 results for “hackathon”
OEMC Hackathon 2023: EU Land Cover Classification Dataset
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset (both train and test) was produced by stratified sampling of the <strong>ground-truth</strong> data provided by LUCAS Survey, funded by the European Commission. The target land cover considered <strong>level-3</strong> classes from the harmonized legend, resulting in <strong>72 classes</strong> distributed over <strong>5 years </strong>(<code>2006</code>, <code>2009</code>, <code>2012</code>, <code>2015</code>, <code>2018</code>):</p> <p>All samples were overlaid with <strong>416</strong> raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.</p> <p><strong>Column names:</strong></p> <p>The columns are formed by six metadata fields separated by <code>_</code>:</p> <ul> <li>Example: <strong>red_landsat.glad.ard_p50_30m_jun25_sep12</strong></li> <li>Metadata fields: <ul> <li>F1 - Variable name: <strong>red</strong></li> <li>F2 - Variable procedure including product name: <strong>landsat.glad.ard</strong></li> <li>F3 - Position in the probability distribution: <strong>p50</strong></li> <li>F4 - Spatial resolution: <strong>30m</strong></li> <li>F5 - Start date: <strong>jun25</strong></li> <li>F6 - End date: <strong>sep12</strong></li> </ul> </li> </ul> <p><strong>Column description:</strong></p> <p>All the columns can be aggregated in six thematic groups according to F1 and F2:</p> <ul> <li><strong>Satellite images (spectral reflectance & vegetation indices):</strong> <ul> <li><code>blue_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat blue band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>blue_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 blue band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>green_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat green band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>mir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 mid-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>ndvi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>nir_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat near-infrared band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>nir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 near-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>red_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat red band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>red_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 red band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>swir1_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>swir2_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Temperature images:</strong> <ul> <li><code>lst_mod11a2.daytime_{..}</code>: Monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime_{..}</code>: Monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>thermal_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat thermal band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Climate layers:</strong> <ul> <li><code>accum.precipitation_chelsa.annual_{..}</code>: Accumulated precipitation over the entire year according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.3years.dif_{..}</code>: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.log.csum_{..}</code>: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.montlhy_{..}</code>: Accumulated precipitation for each month according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>bioclim.var_chelsa.{variable_code}_{..}</code>: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For <code>variable_code</code> descriptions see <a href="https://chelsa-climate.org/bioclim/">chelsa-climate.org</a> (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> </ul> </li> <li><strong>Accessibility & distance maps:</strong> <ul> <li><code>accessibility.to.ports_map.ox.{variable_code}_{..}</code>: Time-required to access ports of different size according to <a href="https://doi.org/10.1038/s41597-019-0265-5">Nelson et al., 2019</a></li> <li><code>burned.area.distance_global.fire.atlas_{..}</code>: Distance to burned areas mapped by <a href="https://doi.org/10.3334/ORNLDAAC/1642">Global Fire Atlas</a></li> <li><code>cost.distance.to.coast_gedi.grass.gis_{..}</code>: Cumulative cost of moving (derived by <a href="https://grass.osgeo.org/grass83/manuals/r.cost.html">r.cost</a>) to the coast</li> <li><code>road.distance_osm.highways.high.density_{..}</code>: Distance to high density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>road.distance_osm.highways.low.density_{..}</code>: Distance to low density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>water.distance_glad.interanual.dynamic.classes_{..}</code>: Distance to permanent / seasonal water bodies according to<br> <a href="https://doi.org/10.1016/j.rse.2020.111792">Pickens et al., 2020</a></li> </ul> </li> <li><strong>Digital terrain model (DTM):</strong> <ul> <li><code>elev.lowestmode_gedi.eml_{..}</code>: Mean estimate of the terrain elevation in <code>dm</code> filtered using <a href="https://saga-gis.sourceforge.io/saga_tool_doc/6.2.0/grid_filter_1.html">SAGA GIS Gaussian filter</a> (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>slope.percent_gedi.eml_{..}</code>: Mean slope in <code>%</code> derived from terrain elevation ([Witjes et al., 2023]</li> </ul> </li> <li><strong>Other existing maps:</strong> <ul> <li><code>pop.count_ghs.jrc_{..}</code>: Annual time-series of population count in number of people mapped by <a href="https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">Schiavina et al., 2023</a></li> <li><code>snow.duration_global.snowpack_{..}</code>: Annual duration of snow occurrence mapped by <a href="https://www.dlr.de/eoc/desktopdefault.aspx/tabid-8297/14218_read-37938/">Global SnowPack</a></li> </ul> </li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 42,237 rows and 420 columns, including sample id (<code>sample_id</code> - index column), land cover code (<code>land_cover</code>), land cover label (<code>land_cover_label</code>), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>test.csv</strong>: Test set with 42,271 rows and 418 columns, including sample id (<code>sample_id</code> - index column), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 42,271 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and predicted land cover code (<code>land_cover</code>)</li> </ul>
AI4EIC Hackathon
<p>AI4EIC 2nd workshop event follows the productive experience of the first AI4EIC workshop held in 2021 at CFNS and is organized by the EICUG AI WG. The scope of this second workshop is to cover all active and potential areas of applications of AI/ML for the EIC. Along with the 2nd workshop, a hackathon is also conducted on the 14th of October 2022 for the whole day.</p> <p>For this hackathon we propose problems of different difficulty, starting from one that is accessible to everyone, that are deemed to be solvable in a one-day event. <br> We focus on the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as part of the particle-identification (PID) system at the future Electron-Ion Collider (EIC). Data are produced using the ePIC software stack.<br> We choose as a working point a range in momentum around 15 GeV, which corresponds to a region where potentially both radiators can contribute to the $\pi/K$ separation (and certainly at larger momenta the gas radiator can contribute to PID). <br> Despite the inherent `simplicity' of the problems, given the approximations made as explained in this document, this event can potentially become a first step towards machine learning/deep learning application for PID with the dRICH.</p>
Hackathons as a Pedagogical Strategy to Engage Students to Learn and to Adopt Software Engineering Practices
<p>Teaching Software Engineering is not a trivial duty since several pedagogical strategies can be used and sometimes the impact of these on students is uncertain. Hackathons are similar to marathons, however used to produce solutions to solve a specific problem in a short period of time and based on intense collaboration. Educational hackathons aim to promote learning in such an environment. The Undergraduate computing programs of PUCRS decided to use a hackathon as a pedagogical strategy aiming to motivate the students to practice the adoption of software development practices and to work in groups as a means to practice the development of social skills. Therefore, we conducted a case study to investigate: 1) The motivations to students to attend or not attend an educational hackathon, 2) The students perceptions about this hackathon, 3) The Software Engineering practices adopted by students. In this study, we identified factors that may affect students motivation to participate (e.g., improve the teamwork skills), some students expectations about the hackathon (e.g., work in teams), and the practices adopted by the students (e.g., pair programming). Some of our findings include that students enjoy participating in an informal educational environment (e.g., hackathons) to improve their technical skills and to build network with some colleagues. This study can provide insights to teachers that wants to organize some activity than traditional teaching and the students perspective about this kind of strategy.</p>
Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science
<p>A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.</p><p>All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.</p><p>Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.</p><h4>Package Data</h4><p>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. </p><p>Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.</p><h4>GitHub Data</h4><p>Two different approaches were taken for collecting data for referenced GitHub mentions:</p><p>1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.</p><p>2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. </p><p>There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.</p><h4>Contact</h4><p>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues</p>
Dataset for An Empirical Study of Hackathon Code Creation and Reuse
<p>This dataset is corresponds to our extended analysis done for “The Secret Life of Hackathon Code Where does it come from and where does it go?” (<a href="https://doi.org/10.1109/MSR52588.2021.00020">https://doi.org/10.1109/MSR52588.2021.00020</a>, pre-print at: <a href="https://arxiv.org/abs/2103.01145">https://arxiv.org/abs/2103.01145</a>) and “Tracking Hackathon Code Creation and Reuse” (<a href="https://doi.org/10.1109/MSR52588.2021.00085">https://doi.org/10.1109/MSR52588.2021.00085</a>, pre-print at: <a href="https://arxiv.org/pdf/2103.10167">https://arxiv.org/pdf/2103.10167</a>). The replication package including the scripts used for generating this dataset from the “World of Code” (https://worldofcode.org/) dataset is available in Github link <a href="https://github.com/woc-hack/track_hack">https://github.com/woc-hack/track_hack</a>.</p> <p>The dataset contains the blob hashes used in the scope of the analysis and the analysis outcome.</p> <p>The columns are as following</p> <ul> <li><strong>DevpostID</strong>: Devpost identification for the hackathon project and it can be used to get the URL for the devpost.com website. Example DevpostID -q9nd5 can be translated to https://devpost.com/software/-q9nd5</li> <li><strong>ProjectID</strong>: The Github project name</li> <li><strong>HackathonEndDate</strong>: Hackathon event end date</li> <li><strong>BlobHash</strong>: The blob hash used in the analysis</li> <li><strong>BeforeHackathon-DuringHackathon-AfterHackathon</strong>: This column represents if the blob was first introduced before/during/after the hackathon (1: before, 2: during, 3: after)</li> <li><strong>SameAuthor-Contributor-OtherAuthor</strong>: This column represents if the blob was first created by someone in the hackathon team, or someone who was a contributor to a project in which one of the members of the hackathon project contributed to as well (contributor), or someone else outside of the hackathon team (1: Author is a hackathon team member, 2: Author Contributed before with a hackathon team member, 3: Author is not related to the hackathon team).</li> <li><strong>UsedBySmallProject-UsedByMediumProject-UsedByLargeProject</strong>: This column represents if the hackathon blob is reused again after the hackathon event and what is the project size that reused the code (1: not reused, 3: reused in small project, 4: reused in medium project, 5: reused in large project, NA: not in scope of the reuse analysis)</li> <li><strong>IsTemplate</strong>: This column represents if the blob is identified as a template file or not ( YES: File is template, NO: File is not template). The template definition and method is explained in the research paper.</li> </ul>
RAW DATA_O4C_Hackathon1_BCN POST-Hackathon 1_Participant Questionnaire_Anonymised
<p>O4C Barcelona pilot's questionnaire of participants before the first O4C hackathon, held from 17th to 19th September 2016. Anonymised Excel sheet of results. This provides an example of questions asked of hackathon participants in the O4C pilots after a hackathon.</p>
RAW DATA_O4C_Hackathon1_BCN PRE-Hackathon 1_Participant Questionnaire_Anonymised
<p>O4C Barcelona pilot's questionnaire of participants before the first O4C hackathon, held from 17th to 19th September 2016. Anonymised Excel sheet of results. This provides an example of questions asked of hackathon participants in the O4C pilots before a hackathon.</p>
Tweet IDs for the #brexit tweet dataset collected for the Helsinki Digital Humanities Hackathon 2019
<p>This dataset contains lists of tweet ids for the tweets used as material by the "Brexit in Transna­tional So­cial Me­dia" group in the <a href="http://heldig.fi/dhh19/">Helsinki Digital Humanities Hackathon 2019</a>.</p> <p>Due to restrictions in Twitter's terms of service, the full tweet dataset cannot be made public. However, Twitter allows the publication of tweet ids, from which the dataset can be reconstituted, <em>with the exception of deleted tweets</em>.</p> <p>The dataset was gathered as follows: Between 2019-01-22T09:19Z and 2019-04-15T14:42Z, a <a href="https://github.com/DocNow/twarc">Twarc</a> version 1.6.1 script was called hourly to retrieve and archive tweets from Twitter matching the #brexit hashtag using the Twitter search API. All 5,547,585 tweet IDs returned by this run are listed in the file <code>original_ids.txt.gz</code>.</p> <p>However, upon further inspection, problems were identified in the archiving. For an unidentified reason, gathering did not occur between 2019-02-13T06:17Z and 2019-02-26T09:24Z. In addition, the Twarc script had been run without the <code>--extended</code> argument, so the tweet data contained only truncated contents for many tweets.</p> <p>Due to this, a decision was taken to rehydrate a new dataset of tweets falling between 2019-02-26T09:24Z and 2019-04-15T14:42Z. Of the original 4,197,059 tweets gathered for this time period (listed in <code>continuous_ids.txt</code>), 3,941,653 could be rehydrated (i.e., they had not been deleted in the interim). The ids of tweets in this dataset are listed in the file <code>continuous_rehydrated_ids.txt</code>.</p> <p>Finally, out of these rehydrated tweets, a subset was derived that filtered out all tweets that were pure retweets. This subset consisted of 1,104,514 tweets, whose ids are listed in the file <code>continuous_rehydrated_no_retweets_ids.txt</code>.</p>
Lymphocyte Assessment Hackathon (LYSTO)
<p>Training and test set of the Lymphocyte Assessment Hackathon (LYSTO), held in conjunction with the Computational Pathology Workshop (COMPAY) at the MICCAI 2019 conference.</p> <p><a href="https://lysto.grand-challenge.org/">https://lysto.grand-challenge.org/</a></p> <p>You can cite the LYSTO study and data using the following reference:</p> <p><em>Y. Jiao et al., "<a href="https://ieeexplore.ieee.org/document/10296868" target="_blank" rel="noopener">LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset</a>," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 3, pp. 1161-1172, March 2024, doi: 10.1109/JBHI.2023.3327489. </em></p>
OEMC Hackathon 2023: Global FAPAR Modeling Dataset (including raster data)
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range <code>0-1</code>. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.</p> <p>For each monthly FAPAR value, a set of covariates / features were extracted from <strong>32</strong> raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the <code>sample_id</code> column, and data points points belonging to the same location are identified by <code>station_number</code>.</p> <p><strong>Column names:</strong></p> <ul> <li><code>sample_id</code>: unique identifier of datapoint</li> <li><code>station</code>: ground station number</li> <li><code>fapar</code>: monthly mean FAPAR</li> <li><code>month</code>: month of measurement</li> <li><code>modis_{..}</code>: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on <a href="https://lpdaac.usgs.gov/products/mod13q1v061/">MOD13Q1</a></li> <li><code>modis_lst_day_p{..}</code>: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>modis_lst_night_p{..}</code>: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on <a href="https://lpdaac.usgs.gov/products/mod11a2v061/">MOD11A2</a></li> <li><code>wv_yearly_p{..}</code>: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_p{..}</code>: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_lt_sd</code>: Water vapour aggregated long-term monthly standard deviation based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_raw</code>: Water vapour monthly time series based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>wv_monthly_ts_smooth</code>: Water vapour monthly time series smoothed using the Whittaker method based on <a href="https://zenodo.org/record/8226282">MCD19A2</a></li> <li><code>accum_pr_monthly</code>: Monthly accumulated precipitation based on <a href="https://doi.org/10.1038/sdata.2017.122">CHELSA timeseries</a></li> <li><code>dtm_{..}</code>: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on <a href="https://ui.adsabs.harvard.edu/abs/2017AGUFM.H12C..04Y">MERIT DEM</a></li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 3,461 rows and 36 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>), measured FAPAR (<code>fapar</code>), and 32 features / covariates</li> <li><strong>test.csv</strong>: Test set with 4,939 rows and 34 columns, including sample id (<code>sample_id</code> - index column), ground station (<code>station</code>), reference month (<code>month</code>) and 32 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 4,939 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and measured FAPAR (<code>fapar</code>)</li> </ul>
PDF4 - Semantics and Vocabularies Hackathon SWOT Visualization
<p>This SWOT visualization outlines the community's feedback regarding the overall polar RDM strengths, weaknesses, opportunities, and threats. This visualization is valuable to convey the overarching themes/ topics of interest, and allows for stakeholders to determine where resources should be allocated. The content for this visualization was compiled through community input during the 'Semantics and Vocabularies' hackathon at the 4th Polar Data Forum in September 2021. </p>
Kenguruji Arnes Hackathon 2024 - Meteorological Data
<p>Hydrological data for Kenguriji team at Arnes Hackathon 2024. The same data are also available in our repository at <code><a href="https://github.com/p1rko/ArnesHackathonKenguruji/tree/wip-models/dataset/meteo" target="_blank" rel="noopener">dataset/meteo</a></code>. Data were obtained from <a href="https://meteo.arso.gov.si/met/sl/archive/" target="_blank" rel="noopener">ARSO archive</a> using a script in our repository.</p>
Hackathon - TF-TG literature triage Gold Standard corpus
<p>This link contains the gold standard corpus for training the systems aiming at classifying or triaging the documents (PubMed abstracts) in relevant or not relevant for TF-TG relations. Note that this is not purely a curation triage, but we are primarily looking whether the abstracts do describe/characterize TF-TG interactions (positive training set labeled as “1” or they don't (negative training set labeled as “0”). </p> <p>This dataset was manually classified by domain experts into these to classes.</p> <p>Note that in PubMed there is a very considerable class imbalance, implying that there is only a very small fraction of TF-TG relations relevant literature subset. The resulting classifier should be useful to triage the entire PubMed database.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562939/files/greekc_triage_training_v02.tsv?download=1">greekc_triage_training_v02.tsv</a></p> </li> <li> <p>Example:</p> </li> </ul> <p>11731616 [{"sourceid":"11731616","sourcedb":"PubMed","text":"Homeobox protein Gsh-1-dependent regulation of the rat GHRH gene promoter. Although GHRH is known to play a pivotal role in the regulation of the GHRH-GH-IGF-I axis, the molecular mechanism of GHRH gene expression has not yet been examined. Here we studied the transcriptional regulation of the GHRH gene 5'promoter using an in vitro experimental model system. We especially focused on the role of homeobox transcriptional factor Gsh-1, because a dwarf phenotype and abolished GHRH expression was observed in Gsh-1 knockout mice. First, we cloned human Gsh-1, which showed 87.3% homology with mouse Gsh-1 at the nucleotide level. When the 5'-promoter region of the rat GHRH gene was introduced into the human placental cell line JEG-3, in which we found the endogenous expression of Gsh-1 as well as GHRH mRNA, substantial transcriptional activity of the promoter was recognized. Promoter activity was further enhanced by overexpression of Gsh-1 protein, whereas it was substantially reduced by elimination of Gsh-1 binding sites. EMSA confirmed the actual binding of Gsh-1 on the multiple binding sites of GHRH gene promoter. Finally, coexpression of CREB-binding protein significantly enhanced the Gsh-1-induced GHRH gene expression, suggesting the cooperative role of the coactivator protein. Because Gsh-1 is found to be expressed in the hypothalamus of the adult rat, our data provide evidence that the Gsh-1 homeobox protein plays a key role in the expression of the GHRH gene.","denotations":[{"obj":"Gene:29446","span":{"begin":84,"end":88}},{"obj":"Gene:288457","span":{"begin":17,"end":22}},{"obj":"Gene:29446","span":{"begin":55,"end":59}},{"obj":"Gene:29446","span":{"begin":146,"end":150}},{"obj":"Gene:29446","span":{"begin":193,"end":197}},{"obj":"Gene:29446","span":{"begin":295,"end":299}},{"obj":"Gene:14842","span":{"begin":430,"end":435}},{"obj":"Gene:14601","span":{"begin":477,"end":481}},{"obj":"Gene:14842","span":{"begin":509,"end":514}},{"obj":"Gene:219409","span":{"begin":553,"end":558}},{"obj":"Gene:14842","span":{"begin":599,"end":604}},{"obj":"Gene:29446","span":{"begin":669,"end":673}},{"obj":"Gene:219409","span":{"begin":783,"end":788}},{"obj":"Gene:2691","span":{"begin":800,"end":804}},{"obj":"Gene:288457","span":{"begin":940,"end":945}},{"obj":"Gene:288457","span":{"begin":1010,"end":1015}},{"obj":"Gene:288457","span":{"begin":1068,"end":1073}},{"obj":"Gene:29446","span":{"begin":1107,"end":1111}},{"obj":"Gene:54244","span":{"begin":1152,"end":1172}},{"obj":"Gene:288457","span":{"begin":1200,"end":1205}},{"obj":"Gene:29446","span":{"begin":1214,"end":1218}},{"obj":"Gene:288457","span":{"begin":1304,"end":1309}},{"obj":"Gene:288457","span":{"begin":1408,"end":1413}},{"obj":"Gene:29446","span":{"begin":1473,"end":1477}}]}] 1</p> <ul> <li> <p>Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positions, corresponding class label of the records (1= relevant, 0=nonrelevant))</p> </li> <li> <p>PubAnnotation format description: <a href="http://www.pubannotation.org/docs/annotation-format/">http://www.pubannotation.org/docs/annotation-format/</a></p> </li> <li> <p>PubTator record retrieval description:</p> </li> </ul> <p>https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.html</p> <p><strong>Remember:</strong> One must notice that this dataset is high imbalanced, with very few cases of the positive class. The additional files could also be explored to build the model or even to build a classifier working at the level of sentences instead of abstracts,</p> <p> </p>
Hackathon - TF-TG literature triage unlabelled data
<p>Once literature triage system is ready it is time to actually try to apply if to records that do not have any label in order to find the subset that does describe TF-TG interactions (are relevant). This is the corpus that has to be labeled by the systems created (hopefully) during the hackathon. To make the results more useful we have pre-selected records that do mention TFs by exploiting either automatic human TF mention recognition or external references from databases that have manually curated information on transcription factors (from GeneRif or UniProt). This means that these abstracts should be enriched with TF relevant records. This record has the same format as the training data except that the last column with the class label is missing.</p> <p>It contains PMIDs and Abstracts.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562913/files/greekc_triage_unlabelled_v01.tsv?download=1">greekc_triage_unlabelled_v01.tsv</a></p> </li> <li> <p>Example:</p> </li> <li> <p>Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positions</p> </li> <li> <p>PubAnnotation format description: <a href="http://www.pubannotation.org/docs/annotation-format/">http://www.pubannotation.org/docs/annotation-format/</a></p> </li> <li> <p>PubTator record retrieval description:</p> </li> </ul> <p>https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.html</p> <p><strong>Warning:</strong> This file is quite big!</p>
Curated data for phage-bacteria hosts for BioML Hackathon
<p>This is the accompanying raw data for this github: https://github.com/havu73/hackathonBio</p> <p>A brief summary of the datasets:</p> <p>For every dataset we have genome sequences for hosts and phages and all positive pairs between phages and hosts (majority of the possible pairs are negative, so we do not save them explicitly). </p> <ul> <li><strong>E.coli: </strong>325 bacterial hosts and 96 phages<strong> </strong>(source study link: https://doi.org/10.1101/2023.11.22.567924)</li> <li><strong>Vibrio: </strong>259 bacterial hosts and 239 phages (source study link: https://doi.org/10.1038/s41467-021-27583-z)</li> <li><strong>Klebsiella:</strong> 149 bacterial hosts and 115 phages (source study link: https://doi.org/10.1038/s41467-024-48675-6)</li> <li><strong>phageDB: </strong>74 bacterial hosts and 4766 phages<strong> </strong>(source link: https://phagesdb.org/)</li> <li><strong>phageScope: </strong>180 bacterial hosts and 4434 phages (source link: https://phagescope.deepomics.org/database)</li> </ul>
Met Office Bath Hackathon Dataset
<p>The dataset contributed by the Met Office for a University of Bath hackathon in September 2023.</p>
Unsupervised-Hackathon
<p>The record contains LHC simulations to be used in the hackathon related with the darkmachines unsupervised challenge project. The documentation about the files content in the tar file is included as well (dataset.pdf).</p>
Can participation in a hackathon impact the motivation of software engineering students? A preliminary case study analysis
<p>This public dataset covers the answers to both pre-hackathon and post-hackathon surveys. Additionally, we offer basic descriptive and inferential statistics related to the provided data.</p>
Metadata crosswalks for software management plans at NFDI4DS hackathon maSMP 2023
<p>Software Management Plans (SMPs) help formalize a set of structures and goals that ensure the research software is accessible and reusable in the short, medium and long term. Although not as common as the Data Management Plans, SMPs are gaining attention, with different communities providing examples and guidance on around it (e.g., ELIXIR SMPs, eScience Center in the Netherlands SMP Guidance and the Max Plank Digital Libraries SMP).</p><p>Machine-actionable SMPs (maSMPs) aim at providing a semantic layer on top of SMPs in the form of metadata schemas. Based on the <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a> and inspired by the work done on the Research Data Alliance (RDA) <a href="https://github.com/RDA-DMP-Common/RDA-DMP-Common-Standard">machine-actionable Data Management Plan (maDMP)</a> application profile, the SemTec team at ZB MED has created an <a href="https://zenodo.org/doi/10.5281/zenodo.7806638">maSMP metadata schema</a> using <a href="https://schema.org/">schema.org</a> as base vocabulary. </p><p>The latest version of such maSMP metadata schema takes advantage of the crosswalks created at the NFDI4DS hackathon on maSMPs 2023.11.28 to 2023-12-01 organized by the SemTec team at ZB MED. The crosswalks data corresponding to Max Plank Digital Library SMP, <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a>, and <a href="https://doi.org/10.14454/3w3z-sa82">DataCite Metadata Schema 4.4 - OutputManagementPlan</a> are included in this dataset collection. The files in the dataset collection are:</p><ul><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - Software.csv" - Crosswalk from the maSMP vr 2.0.0 to the Max Plank Digital Library SMP, <a href="https://github.com/KnowledgeCaptureAndDiscovery/somef">SOMEF</a>, and <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a></li><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - MngPlan.csv" - Crosswalk from the maSMP to <a href="https://doi.org/10.14454/3w3z-sa82">DataCite Metadata Schema 4.4 - OutputManagementPlan</a> and the Max Plank Digital Library SMP</li><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - RDMO-SMP.csv" - Crosswalk from the Max Plank Digital Library SMP to metadata properties from different vocabularies</li><li>"Tables description.png" - Image showing a crosswalk template</li></ul><p>We acknowledge the feedback provided by other participants in the NFDI4DS hackathon on maSMPs (Esteban Gonzalez <a href="https://orcid.org/0000-0003-4112-6825">ORCID:0000-0003-4112-6825</a>, Yves Vincent Grossmann <a href="https://orcid.org/0000-0002-2880-8947">ORCID:0000-0002-2880-8947</a>, Carlos Utrilla Guerrero <a href="https://orcid.org/">ORCID:0000-0002-9994-1462</a>, Mariaisabel Gonzalez-Ocanto <a href="https://orcid.org/0000-0001-5485-9724">ORCID:0000-0001-5485-9724</a>,Thomas Pronk <a href="https://orcid.org/0000-0001-9334-7190">ORCID:0000-0001-9334-7190</a>, David Wallace <a href="https://orcid.org/0000-0001-8958-4601">ORCID:0000-0001-8958-4601</a>, Jürgen Windeck <a href="https://orcid.org/0000-0003-1909-4353">ORCID:0000-0003-1909-4353</a>).</p><p>The maSMP project is part of the NFDI4DataScience project funded by the German Research Foundation (DFG), project number 460234259. This dataset was created during the NFDI4DS hackathon on maSMPs at ZB MED 2023.</p><p> </p>
AI HERO Hackathon 2022 - Team H2 Model Parameters
<p>Model parameters for model created by team H2 at AI HERO Hackathon 2022</p>
ScienceDex guides
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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.