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8,537 results for “Openness”
Replication Package for ASE 2023 Paper "Personalized First Issue Recommender for Newcomers in Open Source Projects"
<p>This replication package contains a replication package for ASE 2023 paper titled "Personalized First Issue Recommender for Newcomers in Open Source Projects." This package includes a dataset of 68,858 issues from 100 GitHub projects, records of 123 manually labeled issue samples, and Python scripts for analyzing the data and evaluating models. The package is also stored in the GitHub repository <a href="https://github.com/mcxwx123/PFIRec">https://github.com/mcxwx123/PFIRec</a>.</p> <p>Required Environment</p> <p>We recommend setting up the required environment on a commodity Linux machine with at least 1 CPU Core, 8GB Memory, and 100GB empty storage space. Our experiments were conducted on an Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>Files and Replicating Results</p> <p>We used the GFI-bot database and the GitHub GraphQL API to collect features of 68,858 candidate issues and restore historical states of resolvers of 11,615 FIs (first issues).</p> <p>The followings are the files and replicating results:</p> <p>Dataset:</p> <p>The raw data of newcomer-issue pairs' features are stored in <code>ReplicationPackage/data/dataset_{bertmodel}_{num}.pkl</code>, where {bertmodel} is one of the four BERT-based language models: SIMCSE, RoBERTa, CodeBERT, and BERTOverflow, corresponding to the dataset whose textual features are extracted by one of the four language models. And {num} is 0 to 19, corresponding to the 20 chronological folds. The training sets of the GFI-Bot approach are contained in <code>ReplicationPackage/data/training_set_recgfi_simcse_{num}.pkl</code>. <code>ReplicationPackage/data/newcomerdata.json</code> contains first issues' title and description and their resolvers' total commit number and number of commits in the latest month, and <code>ReplicationPackage/data/processeddata.pkl</code> contains the 37 developers' features for the empirical study. <code>ReplicationPackage/data/isstexts.json</code> contains issues titles and descriptions for Stanik et al.'s approach.</p> <p>Python scripts:</p> <p><code>ReplicationPackage/empirical.py</code> is the script for reproducing all the results in Section III of the paper. <code>ReplicationPackage/model.py</code> is the script for reproducing all the results in Section IV of the paper.</p> <p>Records:</p> <p><code>ReplicationPackage/PFIs.csv</code> records the manually labeled issues for the empirical study.</p> <p>Figures:</p> <p>By running <code>ReplicationPackage/empirical.py</code> and <code>ReplicationPackage/model.py</code>, you can get all the figures in the fold <code>ReplicationPackage/figures/</code>. Besides the figures in the paper, <code>ReplicationPackage/figures/</code> also contains <code>typedis_{num}.png</code>, and <code>domaindis_{num}.png</code>, {num} is 1 to 4, representing additional results of newcomer features for Figure 4 in the paper.</p>
ARIA Linked Open Dataset part of the WHOW-KG
<p>This linked open dataset contains data of Lombardy, Italy, i.e.:</p> <ul> <li>Analytical data of river water bodies, including flow rate;</li> <li>Analytical data of lake water bodies;</li> <li>Analytical data of groundwater;</li> <li>Height of the lakes;</li> <li>Infectious diseases rates;</li> <li>Meteo observations and weather stations.</li> </ul>
Data for: Butterfly eyespots exhibit unique patterns of open chromatin
<p>How the precise spatial regulation of genes is correlated with spatial variation in chromatin accessibilities is not yet clear. Previous studies that analysed chromatin from homogenates of whole-body parts of insects found little variation in chromatin accessibility across those parts, but single-cell studies of <em>Drosophila </em>brains showed extensive spatial variation in chromatin accessibility across that organ. In this work we studied the chromatin accessibility of butterfly wing tissue fated to differentiate distinct colors and patterns in pupal wings of <em>Bicyclus anynana</em>. We observed that three dissected wing regions showed unique chromatin accessibilities. Open chromatin regions specific to eyespot color patterns were highly enriched for binding motifs recognized by Suppressor of Hairless (Su(H)), Krüppel (Kr), Buttonhead (Btd) and Nubbin (Nub) transcription factors. Genes in the vicinity of the eyespot-specific open chromatin regions included those involved in wound healing and SMAD signal transduction pathways, previously proposed to be involved in eyespot development. We conclude that eyespot and non-eyespot bits of tissue taken from the same wing have distinct patterns of chromatin accessibility, possibly driven by the eyespot-restricted expression of potential pioneer factors, such as Kr.</p>
Article dataset: Taxonomy of Open Science: revised and expanded
<p>Conjunto de dados do artigo: <strong>Taxonomia da Ciência Aberta: revisada e ampliada </strong></p>
NOAA Open Data Dissemination: Petabyte scale Earth System Data in the Cloud
<p>NOAA Open Data Dissemination (NODD) makes NOAA environmental data publicly and freely available on Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP). These data can be accessed by anyone with an internet connection and span key datasets across the Earth system including satellite imagery, radar, weather models and observations, ocean databases, and climate data records. NODD works closely with NOAA stakeholders to make these data available consistently and performantly, often with near-real-time latency. Since its inception, NODD has grown to provide public access to more than 24PB of NOAA data and can support billions of requests and petabytes of access daily. NODD growth is driven by use; stake- holders routinely access more than 5PB of NODD data every month. NODD continues to grow to support open petabyte-scale Earth system data science in the cloud by investing in onboarding additional NOAA data, performant data formats, and streaming data platforms. Here, we document how this pro- gram works with a focus on data provenance, discuss the key datasets available through NODD, show how these data are being used, and highlight ways these data can be accessed with the goal of accelerating use of NOAA resources in the cloud.</p>
Geochemical data for: Intense overpressurization at basaltic open-conduit volcanoes as inferred by geochemical signals: The case of the Mt. Etna December 2018 eruption
<p>The reported dataset is annexed to the article "Intense overpressurization at basaltic open-conduit volcanoes as inferred by geochemical signals: the case of the Mt Etna December 2018 eruption". It consists of five types of parameters: soil CO<sub>2</sub> flux from Mt Etna flanks, CO<sub>2</sub>/SO<sub>2</sub> molar ratio of the volcano plume, SO<sub>2</sub> and HCl fluxes by volcano plume, and He isotope ratio in some peripheral gas emissions, all of them recorded in the period 2017–2019. The data come from continuous monitoring networks installed on Mt Etna and from discrete samplings carried out at specific sites, all the monitoring facilities being supported by the INGV-Civil Defence joint surveillance program.</p>
Open Data for publication Influence of the Time Scale on the Reaction Mechanism of CO Oxidation over a Au/TiO2 Catalyst
<p>The file contains the origianl raw data for publication:</p> <p>Influence of the Time Scale on the Reaction Mechanism of CO Oxidation over a Au/TiO2 Catalyst, Angewandte Chemie, 2023</p> <p>The data contains the IR spectra of the CO2 signal, the IR spectra at different temepratures (modulation experiments) and the IR spectra for different modulation periods at 50 deg C (modulation experiments), as described in the publication.</p>
Chirality-Induced Spin Selectivity: An Enabling Technology for Quantum Applications. Open data set
<p>Data supporting the original figures 5, 6 and 7 of the related publication.</p>
An Open-Source Platform for Head-Fixed Operant and Consummatory Behavior
<p>This is a repository containing all data and analysis codes for Gordon-Fennell et al. 2023 from the lab of Dr. Garret Stuber.</p>
Datasets for Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit
<p>This sdataset contains two zip files, one for the HLA and one for metaproteomics data. These contain the results from individual oktoberfest runs for the publication including the required msms.txt files and percolator outputs.</p>
Benchmarking results for the Open Biomedical Network Benchmark
<p>Aggregated benchmarking results for OBNB</p> <div> </div>
Open tools
<p>List of open tools to support scientific research</p>
3D Printing Concrete Mix Design Open Dataset
<p>3D Concrete Printing Mix Design Open Dataset collected from publications. </p>
Randomly stacked open cylindrical shells as functional mechanical energy absorber
<p>Experimental and simulation data for the paper entitled <em>Randomly stacked open cylindrical shells as functional mechanical energy absorber. </em></p> <p>Download and unzip the AllFigureData.zip. </p> <p>Contact</p> <p>Experiments: sano@mech.keio.ac.jp</p> <p>Simulations: florence.bertails@inria.fr</p>
Associated data underlying the publication "Introducing Open Data Concepts to STEM Students Using Real-World Open Datasets"
<p>While open data concepts become more important in our society, education about its benefits and technical issues is still behind the practice. Students of STEM disciplines should be introduced to open data during their education. The Open Computing course, completely redesigned in the new Computing curriculum, introduces open data concepts, providing both the basics and advanced topics, from technical to social and legal viewpoints. Among the several educational activities, one was particularly useful for understanding the needs and implications of using open data: a synchronous group activity where students had to choose a societal issue, find and analyze two open datasets that would help gaining insight into this issue, assess interdisciplinarity approaches and stakeholders, and finally propose the added value emerging from the solution. In a short amount of time needed, this activity – which tackled multiple aspects of the problem - brought a clearer insight into the topic, building upon the conventional lectures. Students highly graded such an approach to their education, where they had to construct their knowledge by the group experience. A similar group activity appeared to be useful in the context of open data PhD training and might also be used in other disciplines and domains.</p>
Political Participation and Open Data Maturity
<p>To discover a correlation between open data maturity and political participation of European Union countries data about the Open data Maturity, parliamentary voter turnout (%) and individuals using the internet for taking part in online consultations or voting (%) were used.</p>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
The quantification of Open Scholarship - A mapping review
<p>We present the data set for the mapping review "The quantification of open scholarship"</p>
An Open Dataset of Scholarly Publications Referenced in Selected Policy Documents (POLIDOC_SCHOLAR)
<p>POLIDOC_SCHOLAR: An Open Dataset of Scholarly Publications Referenced in Selected Policy Documents</p> <p>This repository contains an open dataset of scholarly publications cited by selected policy documents.</p> <p><strong>1. Background:</strong></p> <ul> <li>We do not aim to create a dataset of references for all policy documents or millions of policy documents but rather from a carefully selected set of policy documents.</li> <li>The long-term plan is to facilitate the inclusion of citations of scholarly publications in open bibliometric databases (or at least to create inter-operable datasets).</li> <li>In the short-term, we plan to increase the number of policy documents included in the dataset and continue to monitor and increase the data quality (completeness of records, provided external identifiers).</li> <li>We will also document - in the next release - the reference extraction process (including code used)</li> </ul> <p> </p> <p><strong>2. Structure of the dataset:</strong></p> <p>The dataset is structured into two primary categories: "<strong>Collections</strong>" and "<strong>Collection References</strong>."</p> <p><strong>Collections:</strong></p> <p>The metadata for selected policy documents is included the "<em>collections.jsonl</em>" file.</p> <p>The <strong>collection</strong> is a central feature of the POLIDOC_SCHOLAR dataset. The selected policy documents are listed in the “<em>collections.jsonl</em>”.</p> <p>For instance, a collection might include reports like the IPCC reports of the 6th Cycle (the "IPCC_AR_6 collection") or the reports from IPBES (the "IPBES collection").</p> <p>Within each collection, there are "documents." These can be twofold:</p> <ul> <li>They represent individual reports within a collection (e.g., the IPCC_AR_6 collection contains 6 reports: 3 assessment reports and 3 special reports from the 6th Cycle of the IPCC assessment).</li> <li>They also denote specific sections of these reports that contain bibliographic references. These sections can be chapters or other segments like supplementary materials or annexes (any section which has a reference list). Each document has a unique code, and the relationships between a main document and its subdivisions are indicated in the "is_part_of" field.</li> </ul> <p><strong>Collection References:</strong></p> <p>To allow users to access only the collections they are interested in, we've separated references by collection in files named "<em>collection_reference_{…name of collection…}jsonl</em>."</p> <ul> <li>Each of these files includes bibliographic references for every document in a specific collection.</li> <li>Besides presenting these as "reference strings" (in their original format within the document), we also offer unique identifiers like DOI and OpenAlex ID to facilitate linkage to external databases.</li> </ul> <p>The documentation of the dataset is provided in the file “<em>data_dictionary</em>”</p> <p><strong>3, Content release v1:</strong></p> <p>This release (POLIDOC_SCHOLAR version 1) includes 2 collections:</p> <ol> <li>IPCC Assessment Cycle 6 </li> <li>IPBES Assessment reports</li> </ol> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>collection</p> </td> <td> <p>Number of reports</p> </td> <td> <p>Number of documents (“sections” with reference)</p> </td> <td> <p>Number of references (strings, not unique)</p> </td> <td> <p>Number of references with DOI (unique)</p> </td> <td> <p>Number of references with DOI (unique)</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>IPCC Assessment Cycle 6</p> </td> <td> <p>6</p> </td> <td> <p>103</p> </td> <td> <p>94,958 </p> </td> <td> <p>51,713</p> </td> <td> <p>48,695</p> <p> </p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>IPBES Assessment reports</p> </td> <td> <p>3</p> </td> <td> <p>27</p> </td> <td> <p>21,750 </p> </td> <td> <p>12,100 </p> </td> <td> <p>11,896</p> </td> </tr> </tbody> </table>
HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Results of building performance simulations
<p>This repository contains the <strong>results of the building performance simulations</strong> carried out in the working package 2.3: Interactions between buildings and open space adaptation measures of the project <a href="http://heatresilientcity.de/">HeatResilientCity II</a>. The buildings under consideration are a multi-residential so-called „Gründerzeithaus“ (GZH) and a large-panel construction (LPC) building. The results were extracted for two rooms of each building on the top floor/attic and follow a consistent name convention. Each file contains hourly resolved values for the outdoor air temperature, the indoor air temperature, the indoor operative temperature and the relative humidity indoor and outdoor. Further information can be found in the README of this repository.</p> <p>In addition to the “plain” simulation results, some <strong>heat-indicator variables</strong> were calculated and listed in <em>Calculate_Indicators_Dresden.txt </em>and<em> Calculate_Indicators_Erfurt.txt</em>. The calculated quantities include temperature weighted exceedance hours for the limits of 25, 26 and 27 °C, the maximum operative temperature occurring during the simulation and the operative indoor temperature and outdoor air temperatures at 4 am and 6 pm on the last day of the heat period/simulation.</p> <p>The used <strong>input data</strong> and <strong>building models</strong> can be found in the related identifiers.</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.