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47 results for “Hackathon”
Climate Informatics 2022 Hackathon Data
<p>This is the dataset for Climate Informatics 2022 Hackathon on drought prediction.</p>
Summative evaluation of LIVE IT 'hackathons'
<p>A report providing an overview of the 'hackathons' procedures, data and results during the LIVE IT.</p>
Kenguruji Arnes Hackathon 2024 - Terrain 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-heightmap/flooding/dataset" target="_blank" rel="noopener">flooding/dataset</a></code>.</p> <p>Included data:</p> <ul> <li>Raw height points were obtained from <a href="https://ipi.eprostor.gov.si/jgp/data" target="_blank" rel="noopener">Public Geodetic Data</a> platform. The exact dataset is available <a href="https://ipi.eprostor.gov.si/jgp-service-api/display-views/groups/85/files/524" target="_blank" rel="noopener">here</a>.</li> <li>Aggregated height maps for 500x500 m2 and 1000x1000 m2 were generated from the raw data with our script, available in the respository.</li> </ul>
Embracing Experiential Learning: Hackathons as an Educational Strategy for Shaping Soft Skills in Software Engineering
<div> <p>This public dataset covers the answers to both pre-hackathon and post-hackathon questionnaires. Additionally, we offer basic descriptive statistics related to the provided data.</p> </div>
Hackathon - TF-TG literature triage additional data
<p>TF = Transcription Factor, TG= target Gene</p> <p>Jamboree: manual triage/TF-TG relation annotation exercise</p> <p>Hackathon: automatic triage/TF-TG relation annotation exercise</p> <p>This link contains three files:</p> <ul> <li> <p>Exp_methods: A list of experimental methods used in the study of transcription factor and target gene interactions. These method names might be useful as semantic features or to index records characterizing experimentally TF-TG relations.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_Exp_methods.zip?download=1">GREEKC_Hackathon_Exp_methods.zip</a></p> </li> <li> <p>Example: TRE_EM_prl_2 Electric Mobility Shift</p> </li> <li> <p>Format: tsv-separated columns (method Id, method name/alias)</p> </li> </ul> </li> <li> <p>Hs_tf_hgnc_ncbigeneid: A list of human transcription factor HUGO gene symbols and their respective Entrez Gene NCBI gene identifier. This gene list might be useful as features for the literature triage systems.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_hs_tf_hgnc_ncbigeneid.zip?download=1">GREEKC_Hackathon_hs_tf_hgnc_ncbigeneid.zip</a></p> </li> <li> <p>Example: ADNP 23394</p> </li> <li> <p>Format: tsv-separated columns (HUGO gene symbol Id, Entrez Gene Id)</p> </li> </ul> </li> <li> <p>Sentences_methods: A list sentences that contain automatically labeled mentions of experimental method that can be used to characterize TF-TG interactions. This dataset might be useful as a more fine-grained additional training set as a considerable number of sentences to describe TF-TG interactions together with the experimental technique used to characterize them.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_sentences_methods.zip?download=1">GREEKC_Hackathon_sentences_methods.zip</a></p> </li> <li> <p>Example: 23675312 A 7 We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes.</p> </li> <li> <p>Format: tsv-separated columns (PubMed ID (PMID), text type (A=abstract, T= Title), sentence number, sentence text string)</p> </li> </ul> </li> </ul>
Hackathon - TF-TG literature triage Baseline Files
<p><strong>Files needed to use the baseline system.</strong></p> <p>Instructions to use the baseline system:</p> <ol> <li> <p>Install DeLFT as explained in <a href="https://github.com/kermitt2/delft">https://github.com/kermitt2/delft</a> (<em>If you are not using GPU, it is </em><em>recomended</em><em> to change the requirements.txt to “tensorflow==1.8.0” instead of “tensorflow_gpu==1.8.0”)</em></p> </li> <li> <p>Download the optimized embeddings (Model_FastText.zip) available in this repository and place it inside the main delft folder.</p> </li> <li> <p>Copy the following files available in this repository:</p> <ol> <li> <p>Greekc.zip: Extract the contents to ./delft/data/models/textClassification</p> </li> <li> <p>greekClassifier.py: Copy to ./delft</p> </li> <li> <p>reader.py: Substitute in ./delft/delft/textClassification/reader.py</p> </li> <li> <p>Embedding-registry.json: Substitute in ./delft/embedding-registri.json</p> </li> <li> <p>Fasttext_300_opt.zip: Extract the contents to ./delft/data/db</p> </li> </ol> </li> <li> <p>To make predictions, change the line 96 in greekClassifier to point to the text you want to predict. Each new line should be an abstract. Then run python3 greekClassifier.py classify > path_to_predicted</p> </li> </ol>
Hackathon - TF-TG relation annotation Gold Standard Corpus
<p>TF-TG relation annotation Gold Standard Corpus.</p> <p>The file contains 130 PMIDs annotated in the following way:</p> <ul> <li>PMID.txt: the original TXT file with the abstract.</li> <li>PMID.ann: the file with the BRAT annotations. Each annotation file contains terms (e.g. T1, T2, ...) and relations (e.g. R1, R2, ...), where each relation is from one term (e.g. T1) to another term (e.g. T2).</li> </ul>
Factorynet Hackathon Dataset 2024
<p>This dataset was for the 2024 ASME-CIE Hackathon featured in this <a title="2024 ASME Hackathon News Article" href="https://www.asme.org/topics-resources/society-news/asme-news/hackathon-winners-used-image-transformers-and-clustering-algorithms-to-achieve-impressive-results">article</a>. The most up to date version of the dataset is hosted on <a title="Factorynet Zenodo Link" href="https://zenodo.org/records/14399422">here on Zenodo</a>.</p>
Care to Share: Dataset and resources for Dutch National Open Science Festival hackathon
<p>This dataset contains the materials used in the session "Care to Share? Investigating Open Science practices adoption among researchers: a hackathon" presented at the Dutch National Open Science Festival on 22nd October 2024.</p> <p>The data files are derived from: Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 ad contains two additional fields (Dimensions_Country and Dimensions_FoR) from Dimensions obtained on 15 October 2024, from Digital Science’s Dimensions platform, available at https://app.dimensions.ai. </p> <h2>File list:</h2> <p><strong>PLOS-Dataset-for-Hackathon.xlsx</strong></p> <p>Data pertaining to the PLOS corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai. </p> <p><strong>Comparator-Dataset-for-Hackathon.xlsx</strong></p> <p>Data pertaining to the Comparator corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.</p> <p><strong>Care to share resource sheet.pdf</strong></p> <p>Document outlining the questions to be investigated during the hackathon as well as key information about the dataset.</p> <p><strong>OSI-Column-Descriptions_v3_Dec23.pdf</strong><br>This file is taken from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686. It describes the fields used in the two data files with the exception of Dimensions_Country and Dimensions_FoR. Descriptions for these are listed in the README tabs of the data files.</p>
data for hackathon geoscience 7 facies model npy format
<p>This dataset is an extraction of zenodo deposit: DOI: 10.5281/zenodo.7244958</p> <p>Dataset will be used for an Hackathon </p>
German-French Hackathon / Data challenge in Passau
Open the record for dataset details and reuse information.
Hackathon_LMMM sif files
Open the record for dataset details and reuse information.
Supplementary material 1 from: Radulovici AE, Vieira PE, Duarte S, Teixeira MAL, Borges LMS, Deagle BE, Majaneva S, Redmond N, Schultz JA, Costa FO (2021) Revision and annotation of DNA barcode records for marine invertebrates: report of the 8 th iBOL conference hackathon. Metabarcoding and Metagenomics 5: e67862. https://doi.org/10.3897/mbmg.5.67862
Figure S1 and Tables S1–S10
AI HERO Hackathon 2022 - Team H5 Model Parameters
<p><strong>Model parameters for model created by team H5 at AI HERO Hackathon 2022</strong></p>
AI HERO Hackathon 2022 - Team H1 Model Parameters
<p><strong>Model parameters for model created by team H1 at AI HERO Hackathon 2022</strong></p>
AI HERO Hackathon 2022 - Team E3 Model Parameters
<p><strong>Model parameters for model created by team E3 at AI HERO Hackathon 2022</strong></p>
AI HERO Hackathon 2022 - Team E2 Model Parameters
<p><strong>Model parameters for model created by team E2 at AI HERO Hackathon 2022</strong></p>
Amazon ML Hackathon 2024
<p>Amazon ML Hackathon 2024 Dataset </p>
AI HERO Hackathon 2023 - Team E4 Model Parameters
<p>Weights of best model for the energy challenge</p>
Kenguruji Arnes Hackathon 2024 - Hydrological Data
<p>Hydrological data for Kenguriji team at Arnes Hackathon 2024. The same data are also available in our repository at <a href="https://github.com/p1rko/ArnesHackathonKenguruji/tree/wip-models/dataset/hydro" target="_blank" rel="noopener"><code>dataset/hydro</code></a>.</p> <p>Included data:</p> <ul> <li>Raw hydrological data obtained from dr. Sašo Petan.</li> <li>Aggregated hourly hydrological data. Data were generated from the raw data with our script, available in the respository.</li> <li>Network graph of river gauges and distances between them. We hand-wrote the graph edges based on the map due to many factors, such as canals, side streams and the missing information about the direction of the rivers. Distances between gauges were calculated with our script, available in the respository.</li> </ul>
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.