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410 results for “Data Repositories”
Data repository
<p>Data repository of the bachelor's thesis <em>T</em><em>he effect of drought on the aboveground biomass of plant functional groups</em></p>
A calibrated groundwater model (Modflow-NWT) data repository in the Koga Irrigation Project area, Ethiopia
<p>The repository includes the research data pertaining to the Modflow-NWT based groundwater model developed for the Koga irrigation project area, Ethiopia. The database constitutes three archived data folders namely, 1. MainData (mostly excel files which include model forcings, data used in model calibration, citizen science data, etc.), 2. GIS (mostly geospatial files to assist readers with the spatial locations of the irrigation project structures, as well as the important data and administrative locations), 3. ModelFiles (mostly text files which include model inputs and outputs).</p> <p>The data has been used in preparation of the manuscript titled, "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment", published in the Agricultural Water Management journal (<a href="http://dx.doi.org/10.1016/j.agwat.2021.106947">10.1016/j.agwat.2021.106947</a>). Readers are requested to go through this article to find more details on the data. The model simulations ranged from 1st January 2008 to 15th August 2019.</p> <p> </p>
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">↩︎</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">↩︎</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
Investigation of metadata standard use by geoscience data repositories
This dataset supports a paper being written about metadata standard use by geoscience data repositories. The study is being done to better understand which metadata standards and keyword vocabularies are prominent within the geoscience data repository landscape. The findings should be useful for NCAR's evaluation of metadata standards within our own systems, as well as by external data repository staff. The guiding questions of the study are as follows: 1. What metadata standards are geoscience data repositories using? 2. What keyword / subject term vocabularies are they using? 3. What interoperability challenges are present in the use of metadata and keyword vocabulary standards within the geoscience repository community?
Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"
This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.
Data Repository for the study of Groundwater Depletion, Food Security, and Power Utility
<p>The present article is based on exploratory and qualitative research methodology for understanding irrigation system with reference to water-energy-food nexus. This article is based on both primary and secondary data. The primary data has been collected through semi-structured in-depth interviews by picking some of the samples from the field. The primary data includes data from the field which is compiled into different sections of the data management plan as follows.</p><ol><li>The socio-economic and demographic profile of the 2 Villages based on the <strong>PRA</strong> (Participatory Rural Appraisal).</li><li>The qualitative data from all types of farmers to understand the common issues and problems of the topic under research (owners as well as non-owners with all socio-economic categories) through <strong>FGD</strong> (Focused Group Discussion).</li><li>The qualitative data from <strong>Cases and Case Studies</strong> to get the deeper understanding of the research of farmers / incidence / processes from the villages to provide some unique insights to the research.</li><li>The qualitative data through <strong>Semi-Structured Interviews</strong> from other stakeholders to understand the issues and problems of the topic under research from their point of view.</li></ol>
Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China"
<h4>1. Title: </h4> <p>Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China".</p> <h4>2. Corresponding author: </h4> <p>Xingbi Lei (leixingbi@st.gxu.edu.cn, leixingbi@foxmail.com).</p> <h4>3. Institution: </h4> <p>Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education, Nanning 530000, China;</p> <p>College of Architecture and Civil Engineering, Guangxi University, Nanning 530000, China; </p> <p>Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530000, China; </p> <p>Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530000, China; </p> <p>State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 200029, China; </p> <p>Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China. </p> <h4>4. Description:</h4> <p>This repository contains gauge data for the research article "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China", which is currently under review for the journal "Atmospheric Research".</p> <p>The gauge data used in this study is the daily measurements (2003/01/01-2018/12/31) of 12 stations in the Chengbi River Basin (Southwest China). The data source was the Chengbi River Reservoir Bureau. The data has been quality controlled by the Bureau in accordance with "MWR, PRC,2015. Specification for precipitation observations: SL 21-2015". Please see the journal article for more detailed information.</p> <h4>5. Citation:</h4> <p>When using this dataset, please cite "Mo, C. et al., 2024. Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China. Atmospheric Research, 297: 107116.10.1016/j.atmosres.2023.107116". </p> <p> </p>
Code and Data Repository for "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments"
<p>The repository includes data and R code necessary to reproduce all figures and analyses presented in the study titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments".</p>
An investigation of data repositories in materials chemistry
<p>Detailed lists of articles used for testing accessibility and raw data used for analysis of data repositories in materials chemistry literature.</p>
Data repository for "Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions"
<p>This Zenodo deposit provides a comprehensive collection of raw data, processed data, and scripts associated with the research paper available at <a href="https://arxiv.org/abs/2306.15186" target="_new">https://arxiv.org/abs/2306.15186</a>.</p> <p>Contents:</p> <p>- data.zip includes all the QMCpack inputs and outputs, which can be used in the blocking analysis to obtain the AFQMC energy. Processed Data "SI for DP-MSD-AFQMC.xlsx" contains the AFQMC results after the blocking analysis in XLSX format. "SI for DP-MSD-AFQMC.xlsx" also includes the energies from all other methods presented in the paper.</p> <p>- scripts.zip contains all the Python scripts used in running calculations for the paper. This includes scripts to generate LAS-AFQMC and CAS-AFQMC trials (under scripts/utils) in QMCpack format, as well as inputs for each compound.</p>
OREN Knowledge Repository: A database of programs, best practices and general data for rural entrepreneurs
<p>The OREN project, <span>Multi-Stakeholder Platform for Rural Entrepreneurs (Project reference No: 2021-1-IT02-KA220-ADU-000033510), is funded by ERASMUS+ through the Italian National Agency Indire and upon completion, expected in 2024, OREN will provide entrepreneurs in the rural sector with a unique toolbox. The tools provided will include courses, guidelines and, most importantly, an integrated platform with business tools where it will be possible to control and coordinate a Business Plan. </span></p> <p><span>Part of the material that was developed for the project were data on best practice for rural entreprises. The current knowledge repository contains two files:</span></p> <p><span>1) OREN_Data_repository_PR1.xlsx: It contains 3 tabs</span></p> <ul> <li><span>IMPORTANT FACTORS: The tab contains important factors that emerged from a desk research on the partner countries of the OREN project. </span></li> <li><span>CASE STUDIES: The tab contains relevant case studies from the partner countries of the OREN project</span></li> <li><span>PROGRAMS: The tab contains relevant programs about rural entrepreneurship from the partner countries of the OREN project</span></li> </ul> <p><span>2) OREN_Data_repository_PR2.xlsx: It contains </span><span>data were grouped into two categories:</span></p> <ul> <li><span>General Data on the area of interest of the partner’s country</span></li> <li>Specific data on rural organizations in the area of interest.</li> </ul> <p> </p> <p> </p>
Netzero2040: Reaching climate neutrality in Austria by 2040: engaging stakeholders for model-supported scenario development. Data repository
<p><span>This repository contains scenario results in pyam format, qualitative scenario narratives and drivers identified by stakeholders for the NetZero2040 project.</span></p> <p><span>NetZero2040 developed the first independent scenarios achieving climate-neutrality in Austria by 2040. We improve on previous analyses by employing a structured co-creation process involving stakeholders and modellers, simultaneously modelling the whole energy system and the electricity system in great detail, creating shared visions of a climate-neutral future. Results are openly available and have been broadly disseminated in the scientific community and to the public. Our scenarios are differentiated by assumptions on energy demand and imports of energy carriers. They show that a rapid electrification of transport and heating, in combination with a build out rate of renewable energies which is well above historical maxima in the Austrian power system, allow significant emission reductions until 2030, and that these measures are consistently required in all scenarios. However, after 2030 scenarios diverge and uncertainty about the most cost-efficient transformation measures prevail. </span></p>
Data accompanying the GitHub repository bartonlab/paper-binary-trait-inference
<p>This dataset contains data from simulations and analysis of within-host HIV-1 evolution that accompany the GitHub repository bartonlab/binary_trait. The GitHub repository contains code and data for reproducing results described in the manuscript "Inferring selection for HIV-1 escape from T cell responses using a binary trait model." See the GitHub repository for details on the interpretation and analysis of this data.</p>
Raw data repository for the article: "Spatially resolved fluorescence of caesium lead halide perovskite supercrystals reveals quasi-atomic behavior of nanocrystals"
<p>Raw data depository for the article in Nature Communications journal: <a href="https://doi.org/10.1038/s41467-022-28486-3">DOI: 10.1038/s41467-022-28486-3</a>. Details and the file description are given in the file "Lapkin_Dataset_Info.pdf"</p>
Network-Nanostructured ZIF-8 too Enable Percolation for Enhanced Gas Transport (Data Repository)
<p>Data repository </p>
Data repository Moringa filber filters npJ clean water
<p>The file contains the experimental data and analysis presented in the manuscript on Moringa functionalized natural fiber filters.</p>
Repository for the paper "Stability of the Jupiter southern polar vortices inspected through vorticity using Juno/JIRAM data"
<p>This repository contains all the *img and *lbl files that have been used in the analysis reported in "Stability of the Jupiter southern polar vortices inspected through vorticity using Juno/JIRAM data". Please see the text for details. </p>
Cosegmentation for Plant Phenotyping+ (CosegPP+) Data Repository Collected Via a High-Throughput Imaging System
<p>CosegPP+ is an extension of CosegPP (https://doi.org/10.5281/zenodo.5117176) with binary masks for a collection of cosegmentation and segmentation algorithms. </p> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p> <table> <tbody> <tr></tr> <tr> <td> <div>Quiñones, R., Samal, A., Das Choudhury, S., & Muñoz-Arriola, F. (2023). OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features. <em>Frontiers in Plant Science</em>, <em>14</em>, 1211409.</div> </td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Data repository of the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by D. Sarria et al.
<p><strong>Data repository of the paper "Library of simulated gamma-ray glows and application to previous airborne observations" by <em>D. Sarria et al.</em></strong></p> <p> </p>
Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects
<p>This is the replication package for the paper “Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects”.</p> <p>In project_curation_selection, there is the curation process of the 100 selected projects including the identification of GitHub repositories and classification of evolution scenarios. </p> <p>In distribution_commits_issues_contributors_market_value_before_after_deploy, data collection from GitHub projects includes the distribution of total commits, contributors, and issues before and after deployment of each investigated project. </p> <p>In analysis_commit_messages, there is qualitative analysis of commit message content from all investigated projects. </p> <p>In the analysis_contributors section, the data focuses on analyzing the profiles of each GitHub contributor involved in the investigated projects.</p> <p>In analysis_market_value_by_project, data refers to the market value and volume of each investigated project. </p> <p>In codes, there are scripts used to obtain the analyzed data.</p> <p> </p>
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.