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6,782 results for “project”
Ashmolean Latin Inscriptions Project (AshLI) EpiDoc files
<p>The EpiDoc files of the Ashmolean Latin Inscriptions Project (AshLI). </p> <p><a href="https://warwick.ac.uk/fac/arts/classics/research/dept_projects/latininscriptions/">https://warwick.ac.uk/fac/arts/classics/research/dept_projects/latininscriptions/</a> </p> <p><a href="https://latininscriptions.ashmus.ox.ac.uk/">https://latininscriptions.ashmus.ox.ac.uk/</a></p> <p> </p>
MERLIN H2020 Project: Interview with Polish participant (2nd podcast)
<p>Wojciech Józefowicz, CEO medVC. June 2019.</p>
MarTREC UTC Data Management Plan Project Name: Green Technology Approach for Capturing Pollution Washed from Transportation Infrastructures Principal Investigator Name(s): Dr. Danuta Leszczynska
<p>This is an original data related to the project sponsored by the MarTREC UTC </p> <p>Title: Green technology Approach for Capturing Pollution Washed from Transportation Infrastructures</p> <p>PI: Dr. Danuta Leszczynska</p>
GAP: Forecasting Commit Activity in git Projects
<p>This entry contains the replication package for our paper <em>GAP: Forecasting Commit Activity in git Projects</em> accepted for publication in Journal of Systems and Software.</p> <p>Abandonment of active developers poses a significant risk for many open source software projects. This risk can be reduced by forecasting the future activity of contributors involved in such projects. Focusing on the commit activity of individuals involved in git repositories, this paper proposes a practicable probabilistic forecasting model based on the statistical technique of survival analysis. The model is empirically validated on a wide variety of projects accounting for 7,528 git repositories and 5,947 active contributors. We found that a model based on the last 20 observed days of commit activity per contributor provides the best concordance. We also found that the predictions provided by the model are generally close to actual observations, with slight underestimations for low probability predictions and slight overestimations for higher probability predictions. This model is implemented as part of an open source tool, called gap, that predicts future commit activity.</p> <p><strong>Replication package</strong></p> <p>The model is explained and defined in <em>"notebooks/Survival analysis.ipynb"</em>. This is a Jupyter notebook created with Jupyter Lab. The dependencies required to run this notebook are listed in <em>requirements.txt</em> and can be automatically installed using <code>pip install -r requirements.txt</code>. Consider making use of a virtual environment to ensure a proper replication of the analyses.</p> <p>The data used to validate the model can be found in <em>data/cargo.csv.gz</em>. They were produced with the script <em>data/convert.py</em> that requires file <em>data-raw/cargo_all_proj_commits_id.csv.gz</em>. This file was created by retrieving all the commits of all projects hosted on github that are related to a project distributed on Cargo. To identify such projects, we relied on libraries.io dataset. GitHub API was then queried to obtain the username of each author (if available) to allow some basic identity merging task. Data about repositories were extracted from libraries.io 1.4.0 dataset and can be found in <em>data/repositories.csv.gz</em>.</p> <p><strong>The GAP tool</strong></p> <p>GAP is made available on <a href="https://github.com/AlexandreDecan/gap">https://github.com/AlexandreDecan/gap</a></p>
Simulation dataset (Project: Spectrum of Public Transit Operations: From Fixed Route to Microtransit)
<p>This dataset is generated for the simulation of three different mobility services along the MTA Bus route B63 in Brooklyn, NY. Each includes the information of origin and destination of passengers within the service area and reflects different levels of demand (80, 200, and 400 passenger/hr). The dataset is produced based on MATLAB.</p>
A mapping between Bot Commit, Projects, Files, and Blobs
<p>This dataset contains information about 150,633,947 file updates by 12,326,137 bot commits, in terms of blob updates.</p> <p>Each line in the file is of the following form:</p> <p>commit_sha; projects (projectOwner_projectName) associated with the commit separated by comma; filename; new blob associated with the file; old blob associated with the file (before the commit)</p> <p>Example:<br> 00000a0cee231959811e6c5d808730240b325417;guyellis_plant;package.json;fadda016e338f970adc62395e77aab80b09fa11b;2e7b221f2bfdeda4ef78d3a56e039e456b6901bb</p> <p><br> Contents of each blob can be accessed using the GitHub API:</p> <p>https://api.github.com/repos/<projectOwner>/<projectName>/git/blobs/<blob id></p> <p>Example:</p> <p>https://api.github.com/repos/guyellis/plant/git/blobs/fadda016e338f970adc62395e77aab80b09fa11b</p> <p>See <a href="https://developer.github.com/v3/git/blobs/">https://developer.github.com/v3/git/blobs/</a> for more deatils.</p> <p>If you're using our data, please do not forget it cite it !!!</p> <p> </p>
Project B2 - Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data
<p>The purpose of this research is to evaluate work zone mobility using SHRP2 NDS naturalistic driving data. In this study, forward, rear-view video, and time series traces for traversed work zones are collected at 0.1 second intervals. Using the forward and rear-ward videos work zone configurations such as traffic control devices, area types, presence of dynamic message signs (DMS), intelligent transportation systems technologies (ITS), as well as the presence of workers and equipment are identified. These videos are also used for estimating traffic density by observing the number of vehicles surrounding the participant, and average traffic flow by observed speed and traffic density. The capacities of different work zone sections and configurations are estimated based on developed speed-flow relationships and this capacity is used to verify and calibrate the work zone capacity method defined by the newest addition of Highway Capacity Manual (HCM). The probability of breakdown is then estimated for each level of flow rates and volume to capacity ratios can be used to verify the results of HCM.</p>
Innovative Healthcare Mobility Services dataset (STRIDE project A2)
<p>This dataset compiles the results of an environmental scan completed in December 2018 (and finalized in 2019) to catalog any documented instance where innovative technology (e.g. ridehailing) was being used for health care transportation. This is part of STRIDE project A2 titled “Changing Access to Public Transportation and the Potential for Increased Travel.”</p>
Route-Level Ridership and Frequency Data in Four Transit Agencies (STRIDE project A2)
<p>This dataset contains route-level ridership and frequency data between 2012 and 2018 in TriMet (OR), Metro Transit (MN), Miami-Dade Transit (FL), and MARTA (GA). Total daily passenger boardings and vehicle-trips are shown for weekdays over time.</p>
Preliminary results from the EntoGEM project May 06, 2020
<p>This release of <a href="https://github.com/entogem/preliminary_results">https://github.com/entogem/preliminary_results</a> contains comparisons of the 10+-year studies identified by EntoGEM as of May 06, 2020 to the references included in 53 previous articles, commentaries, meta-analyses, and other syntheses mentioning "insect decline" in 2019 and 2020.</p> <p>It is <strong>not</strong> a definitive guide to studies that have been neglected by current debate surrounding insect decline, and is simply a checkpoint to verify that the community-driven approach to synthesis is helping identify studies that were previously not part of the conversation.</p>
DATS 6401 - Individual Project - Caroline Sklaver
<p>Top country healthcare, education, and military spending from 2013 to 2018. A visual analysis using HTML, CSS, JS. </p>
dataset location test project
<p>Just a test of an idea</p>
Figure 3 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 3 Schematic diagram of Metadata 2020 Project 6: Metadata Evaluation and Guidance.
Figure 1 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 1 Metadata dialects, recommendations, and communities.
Figure 2 from: Habermann T (2020) Metadata 2020 Metadata Evaluation Projects. Research Ideas and Outcomes 6: e54176. https://doi.org/10.3897/rio.6.e54176
Figure 2 Schematic diagram of Metadata 2020 Project 2: Metadata Recommendations / Element Mappings
Sample dataset TRIT (CHARIOT project)
<p>A dataset with historical data (in .EXCEL files) from Trenitalia existing Dynamic Maintenance Management System (DMMS)</p>
Tokenized and POS-Tagged Khmer Data of the Asian Language Treebank Project
<p>* Introduction</p> <p>This is the Khmer ALT of the Asian Language Treebank (ALT) Corpus. English texts sampled from English Wikinews were available under a Creative Commons Attribution 2.5 License.</p> <p>Please refer to<br> http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/index.html<br> for an introduction of the ALT project.</p> <p>Khmer ALT has been developed by NICT and NIPTICT. The license of Khmer ALT is</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License<br> https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p><br> * Contents</p> <p>- data_km.km-[tok|tag].nova : tokenized/POS-tagged Khmer sentences by the nova annotation system<br> # based on the following two guildelines<br> # http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/Khmer-annotation-guideline.pdf<br> # http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/Khmer-annotation-guideline-supplementary.pdf</p> <p><br> * Disclaimer</p> <p>[1] The content of the selected English Wikinews articles have been translated for this corpus. English texts sampled from English Wikinews were available under a Creative Commons Attribution 2.5 License. Users of the corpus are requested to take careful consideration when encountering any instances of defamation, discriminatory terms, or personal information that might be found within the corpus. Users of the corpus are advised to read Terms of Use in https://en.wikinews.org/wiki/Main_Page carefully to ensure proper usage.</p> <p>[2] NICT bears no responsibility for the contents of the corpus and the lexicon and assumes no liability for any direct or indirect damage or loss whatsoever that may be incurred as a result of using the corpus or the lexicon.</p> <p>[3] If any copyright infringement or other problems are found in the corpus or the lexicon, please contact us at alt-info[at]khn[dot]nict[dot]go[dot]jp. We will review the issue and undertake appropriate measures when needed.</p>
French research projects funded on climate studies and forest research between 1997 and 2017
<p>Exhaustive list of 163 projects used to analyse how French research agencies funded climate studies in forest research. Each project is referenced by its acronym, full title, beginning/end year, disciplinary approaches, use of social or economic approaches, research main focuses, integration of mitigation issues, and website. One project may have two main focuses of the same class, although sometimes only one main focus could be determined, as the projects mentioned only one objective (e.g., “MACCLIF”) or explored a main focus that was too broad to be further refined into a subcategory (e.g., “INFORMED”)</p>
Results of the AbuMIP experiments: The Antarctic BUttressing Model Intercomparison Project
<p>This archive provides the model result of the AbuMIP experiments: The Antarctic BUttressing Model Intercomparison Project.</p>
Stacked Dense Denoise-Segmentation CGLS synthetic reconstruction from 91 projection without ring artefacts
<p>The CGLS recontruction without ring artefacts from the 91 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
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.