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3,225 results for “Case of study”
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study
<p><strong>Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.</strong></p> <p>Contents:<br> 1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;<br> 2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;<br> 3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.</p> <p> </p> <p><strong>Installation guide:</strong></p> <p> 1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p> 4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p> 5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html</p>
Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem - the dataset
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Schinias Case Study Area
<p>Data collected by the SUBSOL Data Monitoring System for the Schinias case study.</p>
Leveraging Commercial Cubesat Constellations for Auroral Science: A Case Study
<p>Planet satellite magnetometer data to accompany the paper "Leveraging Commercial Cubesat Constellations for Auroral Science: A Case Study"</p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)
<p>Data used in manuscript "Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)" for Geoscientific Model Development. </p> <p>Uploaded data includes GETM output and used source code (FABM+GOTM+GETM).</p>
Barriers to crop diversification across 25 European case-studies (dataset)
<p>The data set presents the presence or absence of barriers to crop diversification at different levels of value chain across contrasted European case-studies. Data were collected based on participatory workshops involving a causal analysis of challenges of crop diversification and complementary interviews with the 25 innovations teams of case-studies at the chore of the DiverIMPACTS project (https://www.diverimpacts.net/). These data account for the initial perception of barriers in the initial phase of the innovation project and may have evolved since.</p> <p>For more information about the data collection and project and further discussion, please refer to the paper "<strong>Innovating within or outside dominant food systems? Different challenges for contrasting crop diversification strategies in Europe</strong>" of Morel et al. submitted to the journal Plos One.</p>
Redwood Connectivity Case Study
<p>Data and code for the Redwood Case Study that accompanies "Apps can help bridge restoration science and practice", by Sperry, Shaw, and Sullivan (2019) in Restoration Ecology.</p>
TopicRes Dataset: Brazil Case Study
<p>This repository includes data set and source code used in "Topic Resilience and the Spread of News: Brazilian Cities Case Study".</p> <p><strong>Raw Data.zip file</strong>: Contains news publications collected from Brazilian cities through December 12th, 2018 to March 26th, 2019.</p> <p><strong>News Websites.zip file</strong>: Contains the address list of the online newspapers and blogs used to collect the dataset.</p> <p><strong>Source code.zip file</strong>: Contains four Jupiter files with the source code of each step of the TopicRes framework.</p>
Assessing the Resilience of Software Systems by Application of Chaos Engineering - A Case Study
<p>This repo contains all supplementary data sets that I have created and used throughout my bachelor's thesis. In particular, it contains<br> - JMeter configuration file<br> - CSV files of measured JMeter runs<br> - Chaos Experiments declared in JSON<br> - Created boxplots and ecdf plots based on the CSV files<br> - Kubernetes deployments<br> - Bash scripts <br> </p>
РИС. 10. Относительная ваЖность предикторов для моделей распространения Brephulopsis.cylindrica. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 10. Относительная ваЖность предикторов для моделей распространения Brephulopsis.cylindrica.
РИС. 7. Пригодность местообитаний для Xeropicta derbentina согласно ансамблевой модели. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 7. Пригодность местообитаний для Xeropicta derbentina согласно ансамблевой модели.
Fig. 4 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 4. Lead content (ppm) of the top metre of peat at Holcroft Moss.
Fig. 1 in Benthic Foraminiferal Assemblages and Biotopes in a Coastal Lake: The Case Study of Lake Varano (Southern Italy)
Fig. 1. Location map of the study area with sampling stations.
Fig. 8 in Benthic Foraminiferal Assemblages and Biotopes in a Coastal Lake: The Case Study of Lake Varano (Southern Italy)
Fig. 8. Distribution of the five sub-biotopes as identified by Cluster Analysis.
Figure 6 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 6. Weekly numbers of monitored chukars released into the study area.
Figure 3 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 3. Monitoring station in the research area.
Figure 5 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 5. Responses of birds to project team members during the first month after release.
Figure 4 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 4. Natural predators of the chukars that visited the aviary on the first day of the study.
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.