Find research datasets worth reusing

Output Data from the CLASS model, the CLASS-L model, and the LES.

<p>These data sets are for the work documented in Modifications to the CLASS Boundary Layer Model for Improved Interaction between the Mixed Layer and Clouds (https://doi.org/10.1029/2023MS004202).</p> <p>Please download these data first to run "Plotcode_Open.m" available at https://doi.org/10.5281/zenodo.11186607.</p>

opencc-by-4.0Mar 2024View details →

Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains: Data

<p>This repository contains the data for the following paper:</p> <p><span>Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, et al.. 2024. <a href="https://aclanthology.org/2024.lrec-main.827">Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains</a>. In <em>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</em>, pages 9463–9476, Torino, Italia. ELRA and ICCL.</span></p> <p>1) pretraining data for the Jargon specialized language models</p> <p>2) ECTHR_FR dataset for text classification in the French legal domain</p> <p> </p>

Data from Climate adaptability in hydrological models: variable storage capacity to improve performance under contrasting climates.

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EGFs, gravity and crustal models data around the JPH volcanic area in NE China

<p>The observed EGFs, complete Bouguer gravity anomalies data and the 3-D crustal Vs and density models from our joint inversion around the Jingpohu volcanic area in Northeast China. </p>

opencc-by-4.0Oct 2023View details →

OA Book Usage Data Trust Business Model Canvas - 2023 Discussion Draft

<p>Despite the "Business Model Canvas" (BMC) monikor, this diagram presents sustainability-model elements related to a not-for-profit International Data Space focused on open access book usage data exchange. This diagram was inspired by an initial 2021 draft version created by Murphy et al (<a href="https://doi.org/10.5281/zenodo.6227423" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.6227423</a>) and includes information known in 2023 by OA Book Usage Data Trust project team members. The BMC diagram organizes and presents information about potential partners, activites, resources, value propositions, relationships, outreach, and users alongside anticipated costs and potential cost recovery mechanisms. </p> <p>This work was made possible through the "OAeBU Data Trust: Advancing to Launch by Developing IDS Governance Building Blocks" project grant funded by The Mellon Foundation.</p>

dryad32/100

Data Archival for Economic Cost Modeling of Chinook Habitat Restoration in the Stillaguamish River Basin

<p>We used geospatial data to model economic cost estimates of habitat restoration in the Stillaguamish River Basin in the Puget Sound. We utilized data pertaining to the streams/rivers, floodplain habitat, subbasins, elevation, distance to roads, demographics, and land use within the Stillaguamish River Basin to do so. Analysis included using the different attributes of the Stillaguamish River Basin to create low and high cost estimates for floodplain, engineered log jam, and riparian planting habitat restoration. We specifically looked at the slope and size of streams, area of habitat that needed to be restored, slopes of the riparian area, distance to nearest road, and canopy angles as our model inputs. We followed cost estimate guidance provided by the Puget Sound Shared Strategy to identify our cost ranges and updated them to todays prices using the producer price index. An additional land use analysis was performed to quantify the total area and cost of potential agricultural land in the basin. Lastly, we investigated the demographics of the region to identify areas of POC and low income in relation to proposed restoration actions.</p>

opencc-zeroMay 2024View details →

opencc-by-nc-4.0Feb 2024View details →

Data From: Investigation of nonlocal granular fluidity models using nuclear magnetic resonance

<p>This data set contains the rheo-NMR data presented in the article: Clarke D.A., Poata, J., Galvosas, P., and Holland, D.J. Investigation of nonlocal granular fluidity models using nuclear magnetic resonance. <em>Physics of Fluids</em> 1 May 2024; 36 (5): 053317. <a href="https://doi.org/10.1063/5.0203032" target="_blank" rel="noopener">https://doi.org/10.1063/5.0203032</a></p> <p> </p> <p>Edit History:</p> <ul> <li> 01-MAR-2024: Updated title to match change made to submitted article.</li> <li> 09-MAY-2024: Included journal issue information and DOI.</li> </ul>

Modelling input data for the case study of the paper "Uncertainty-Based Market-Clearing Models: A Comparative Analysis of the Dutch, French, and German Markets".

<p>This data package includes the modelling input data to replicate the results of the case study included in the paper "Uncertainty-Based Market-Clearing Models: A Comparative<br>Analysis of the Dutch, French, and German Markets". </p> <p>The case study models the Dutch, French and German day-ahead electricity markets, in which the existing capacities of electricity generation and upward- and downward reserve capacities are considered, in addition to 105 wind output realization scenarios for each simulation day. A detailed description of the case study is provided in the readme file.</p> <p>This supplementary data package includes the following files:</p> <p>-        Meta Data – Netherlands.xlsx: Dataset containing the meta data for the Dutch case study</p> <p>-        Meta Data – France.xlsx: Dataset containing the meta data for the French case study</p> <p>-        Meta Data – Germany.xlsx: Dataset containing the meta data for the German case study</p> <p>-        Readme.txt: Includes a detailed description of the data packages</p>

Data for "Improving semantic video retrieval models by training with a relevance-aware online mining strategy"

<p>This repository contains all the data available for the publication:</p> <p><a href="https://doi.org/10.1016/j.cviu.2024.104035">Alex Falcon, Giuseppe Serra, and Oswald Lanz. <em>Improving semantic video retrieval models by training with a relevance-aware online mining strategy</em>. <strong>Computer Vision and Image Understanding</strong>. 2024.</a></p> <p>Code is available at: <a href="https://github.com/aranciokov/ranp/">https://github.com/aranciokov/ranp/</a></p> <p>The data includes:</p> <ul> <li>pre-extracted features (ordered_feature_*.zip files)</li> <li>annotations, such as pre-extracted semantic graphs, glove checkpoints, class annotations, etc (annotations_*.zip files)</li> <li>train/val/test, when available, split information (public_split_*.zip) files</li> <li>pretrained models for HGR and EAO (details in the github repo)</li> </ul>

Data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets"

<p>GCM data for the paper "Longitudinal Filtering, Sponge Layers, and Equatorial Jet Formation in a General Circulation Model of Gaseous Exoplanets" published in Monthly Notices of the Royal Astronomical Society.   Data are in the pp format used by the Met Office Unified Model GCM and can be loaded using the Iris python package.<br><br>The individual files correspond to simulations done in the following parts of the paper<br><br>eta75_study.tgz - Simulations from Section 3.1.1 investigating changing the value of t_K with eta_s=0.75<br>Keff_flat.tgz - The simulation with a flattened Keff, as outlined in Section 3.1.2<br>eta9_study.tgz - Simulations from Section 3.2 investigating increasing eta_s to 0.9<br>resolution_study.tgz - The simulations from the resolution study found in Appendix B.</p>

Figures and Data for "CO2 rock physics modeling for reliable monitoring of geologic carbon storage"

<p>The following data includes all data used to generate figures in this study. We will update the link to the paper once it is published. It has been accepted in Nature Comm Earth and Environment. LANL has approved this release: LA-UR-24-25434.</p>

opencc-by-4.0Feb 2023View details →

NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)

<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>

F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems

<p>F-DATA is a novel workload dataset containing the data of around 24 million jobs executed on <a href="https://www.r-ccs.riken.jp/en/fugaku/">Supercomputer Fugaku</a>, over the three years of public system usage (March 2021-April 2024). Each job data contains an extensive set of features, such as exit code, duration, power consumption and performance metrics (e.g. #flops, memory bandwidth, operational intensity and memory/compute bound label), which allows for a multitude of job characteristics prediction. The full list of features can be found in the file <code>feature_list.csv</code>.</p> <p>The sensitive data appears both in anonymized and encoded versions. The encoding is based on a Natural Language Processing model and retains sensitive but useful job information for prediction purposes, without violating data privacy. The scripts used to generate the dataset are available in the<a href="https://github.com/francescoantici/F-DATA"> F-DATA GitHub repository</a>, along with a series of plots and instruction on how to load the data.</p> <p>F-DATA is composed of 38 files, with each <code>YY_MM.parquet</code> file containing the data of the jobs submitted in the month MM of the year YY. </p> <div> <div>The files of F-DATA are saved as <code>.parquet</code> files. It is possible to load such files as dataframes by leveraging the <code>pandas</code> APIs, after installing <code>pyarrow</code> (<code>pip install pyarrow</code>). A single file can be read with the following <code>Python</code> instrcutions:</div> <br> <blockquote> <div><code># Importing pandas library</code></div> <div><code>import pandas as pd</code></div> <div> </div> <div><code># Read the 21_01.parquet file in a dataframe format</code></div> <div><code>df = pd.read_parquet("21_01.parquet")</code></div> <div><code>df.head()</code></div> </blockquote> <div> </div> <div>Please cite this work as:<br><br> <div> <div>@article{antici2025fdata,</div> <div>title={F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems},</div> <div>author={Antici, Francesco and Bartolini, Andrea and Domke, Jens and Kiziltan, Zeynep and Yamamoto, Keiji},</div> <div>journal = {Scientific Data},</div> <div>volume={12},</div> <div>pages={1321},</div> <div>year={2025},</div> <div>publisher={Nature Publishing Group},</div> <div>doi={https://doi.org/10.1038/s41597-025-05633-1}</div> <div>}</div> </div> </div> </div>

Replication data for the paper "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks"

<p>This is a replication data for the paper titled "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks" submitted for a blind review.</p> <p>Abstract</p> <p>The rapid advancement of generative Artificial Intelligence (AI) has significantly transformed various research domains. This paper introduces a novel, fully automated methodology for applying Large Language Models (LLMs) to psychological text analysis. The approach includes prompt design for zero-shot and few-shot learning, model internal consistency analysis, autonomous machine evaluation, and additional human validation. Applied to four psychological theories—Self-Determination Theory, the Big Five Personality Traits, Psychological Well-being, and Cognitive Behavioral Therapy—this methodology is tested on a dataset of 25,780 emails written by a senior executive (called Person X) over 16 years. The analysis involves extracting psychological characteristics from the emails and regressing these characteristics against personal, professional, and environmental factors. The results demonstrate that the methodology provides unique insights into the examined psychological theories, offering a detailed understanding of how various factors influence psychological states and traits over time. This research highlights the potential of LLMs in capturing and analyzing complex psychological patterns in large text corpora, contributing a robust framework for future studies and practical applications in psychological assessment and intervention. The findings underscore the transformative impact of generative AI in psychological research, opening new avenues for understanding human behavior through advanced language models.</p> <p>The zipped file contains five csv files:</p> <ol> <li>Email_classification-csv: LLM (GPT-3.5 Turbo) classification of 25,780 emails for four psychological theories: SDT, Big Five, PWB and CBT.</li> <li>SDT_regression_data.csv</li> <li>Big_Five_regression_data.csv</li> <li>PWB_regression_data.csv</li> <li>CBT_regression_data.csv</li> </ol> <p>For 2-5 files the dependent variable is monthy percentage share of emails the were assigned a given value for categories of one of the four psychological theories analyzed. </p> <p>Linear regression model has been applied, where dependent variable is the percentage of emails in a specified category that assigned a specific value in this category. For example in Big Five Traits Model, for the Openness category, for each month we calculated percentage of emails that exhibit <em>High</em> or <em>Low</em> openness, or <em>None</em> if the content of the email does not provide enough information to assess whether the specific need is relevant. Two dependent variables were created: <em>Openness-high</em> and <em>Openness-low</em> and regressed on all independent variables. Regressions were not run for the <em>None</em> values.</p> <p>Descriptions of independent variables:</p> <p>- <em>income_index</em>: Person X salary income and consulting fees in a given month, normalized to [0,1].</p> <p>- <em>card_spending</em>: Person X credit card expenditures in a given month, normalized to [0,1].</p> <p>- <em>abroad_far</em>: dummy variable set to 1 for months when Person X worked in Central Asia</p> <p>- <em>abroad_near</em>: dummy variable set to 1 when Person X worked in other EU country</p> <p>- <em>death_1_war</em>: variable set to 1 in a month when Person X’ farther in law passed away. In the same month Russia invaded Ukraine. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>death_2</em>: variable set to 1 in a month when Person X’ mother passed away. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>court_case</em>: dummy variable set to 1 for months with the emotionally engaging inheritance court case involving other family members.</p> <p>- <em>BIG4_partner</em>: dummy variable set to 1 for months when Person X worked as a partner in BIG4 accounting firm, which resulted in adopting a professional activity sharply different from the usual Person X habits.</p> <p>- <em>AI_company</em>: dummy variable set to 1 for months when Person X worked as C-level executive at a company specializing in artificial intelligence.</p> <p>- <em>elections</em>: dummy variable set to 1 for months when Person X unsuccessfully run in parliamentary elections</p> <p>- <em>covid_lockdown</em>: dummy variable set to 1 for month where Polish government imposed tough measures during two covid lockdowns.</p> <p>- <em>no_receive</em>: number of different email recipients each month, normalized to [0,1].</p> <p>- <em>avg_length</em>: average number of words in emails sent each month, normalized to [0,1].</p> <p>            While the email data was collected for January 2008 – March 2014 period, financial data was available from October 2009. There were some months where no emails with more than 10 words were sent, yielding 166 monthly observations used for regressions, before removing outliers.</p> <p>            Independent variables were tested for multicollinearity, outlier months were removed, regressions were estimated with robust standard errors, and a range of standard tests were conducted for normality and autocorrelation of residuals, confirming good statistical properties of estimated models.</p> <p>Due to privacy concerns, the email texts cannot be publicly shared. However, the classifications of psychological categories derived from the email texts, along with all other relevant data, are made publicly available in this open access repository, with the consent of email author.</p> <p> </p>

Trained Random Forest model and scaler parameters on new physical and tsfel features from seismic data of 150s length.

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Processed data for the manuscript: Promoting Multi-Task Learning as a General Approach for Deep-Learning-based Hydrological Models

<div> <div>Below is a brief overview of the processed data in this repository:</div> <br> <div>- camels_streamflow: This directory contains streamflow data for CAMELS basins covering the period from January 1, 2015, to December 31, 2021. We have not included the original CAMELS dataset, which contains attributes, meteorological forcing, and streamflow data from January 1, 1980, to December 31, 2014, as it can be easily downloaded from the CAMELS website (https://gdex.ucar.edu/dataset/camels.html) and is too large for us to upload to Zenodo.</div> <div>- modiset4camels: This directory includes multiple versions of basin-mean Evapotranspiration (ET) data retrieved from the MOD16A2 data product. The dataset spans from January 1, 2001, to December 31, 2021, with an 8-day temporal resolution.</div> <div>- nldas4camels: This directory contains basin-mean daily meteorological forcing data from the NLDAS-2 dataset, obtained via Google Earth Engine (GEE). The dataset covers the period from January 1, 2001, to December 31, 2021.</div> <div>- smap4camels: This directory features basin-mean Soil Moisture (SSM) data from the NASA-USDA Enhanced SMAP Global Soil Moisture dataset, covering the period from April 2, 2015, to October 3, 2021. The dataset provides SSM measurements at a 5 cm depth. Additionally, we provide basin-mean daily SMAP L4 data spanning from April 1, 2015, to December 31, 2023.</div> </div>

Training data for building a machine learning wildfire model over the CONUS

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Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'

<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area  </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>

Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'

<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area  </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains data, scripts, and code. It is organized into two main directories: <em>Materials and Methods,</em> and <em>Results</em>.</p> <h2>Materials and Methods</h2> <p>The raw data can be accessed in the <em>Materials_and_Methods/Data directory</em>. The processed data used for modeling is available in <em>Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData</em>. All scripts for data processing are located in <em>Materials_and_Methods/Processing_scripts</em>. The <em>Plots_and_Figures </em>directory<em> </em>includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is <em>Main_script.R</em>.</p> <h2>Results</h2> <p>The <em>Results</em> directory contains three subdirectories and the script <em>Models_Outputs.R</em>. The <em>Results/Fitted_models</em> directory includes all .RData files with fitted models for each species. The script <em>Models_Outputs.R </em>generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the <em>Results/Plots </em>folder, while <em>Results/RData</em> contains intermediate .RData files created by <em>Models_Outputs.R</em> to manage computational costs.</p>

TCO model data

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