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3,172 results for “public”
Synthetic bus validation of Rennes public transportation network focus on Beaulieu campus
<p>Synthetic data based on the STAR/Keolis open data version GTFS_2021.1.0.3_20210830_20211017 for regular weeks and GTFS_2020.10.1_20210705_20210829 for holidays. This dataset contains the generation of bus validations for 3500 individuals during 14 weeks with an average of 15 travels per week. It also contains GPS information of the bus stops associated to the validation.</p>
Data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler.
<p>This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.</p> <p>The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.</p> <p>The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.</p> <p>The data sets are arranged in the following directories:</p> <p>mp2/ : Data sets for the Hartree-Fock Moller-Plesset2 level calculations</p> <p>dmol/ : Data sets for the Density Functional Theory calculations with the DMOL code</p> <p>nlh/ : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data sets</p> <p>zbl/ : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculations</p> <p>zbluniv/ : A directory with a Linux bash/awk script that generates the ZBL universal potential.</p> <p>zblspec/ : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.</p> <p>Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.</p>
Public Utility Data Liberation Project (PUDL) Data Release
<h2><strong>v2025.10.0 (2025-10-14)</strong></h2> <p>This is a regular monthly data release, primarily intended to ensure that PUDL has the most up-to-date EIA-860M data. It also happens to include final EIA-860 data for 2024, and some newly integrated EIA-923 financial data and PHMSA natural gas data. See below for details.</p> <h3>Expanded Data Coverage</h3> <h4>EIA-860</h4> <ul> <li> <p>Updated EIA-860 with final release data from 2024. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4616">#4616</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4617">#4617</a>.</p> </li> </ul> <h4>EIA-860M</h4> <ul> <li> <p>Updated EIA-860M monthly generator report with newly published data for August of 2025. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4639">#4639</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4638">#4638</a>.</p> </li> </ul> <h3>New Data</h3> <h4>PHMSA</h4> <ul> <li> <p>Added eight transformed table containing annual data from PHMSA natural gas distributors from 1970 to the present. Note that these containing mostly numeric values are named as <code><span>_core</span></code> - indicating that these tables have not been fully cleaned and validated. We’ve published these tables to make the 50+ years of PHMSA data we’ve extracted and mapped available for others to use and for contributors to more easily improve incrementally. See <a href="https://github.com/catalyst-cooperative/pudl/issues/3770">#3770</a> and <a href="https://github.com/catalyst-cooperative/pudl/pull/4005">#4005</a>.</p> </li> <li> <p>The first cleaned table, <code><span>core_phmsagas__distribution_operators</span></code> has been added to our PUDL database. Thanks to <a href="https://github.com/sponsors/seeess1">@seeess1</a> for all of your work on this!</p> </li> </ul> <h4>EIA 923</h4> <ul> <li> <p>Thanks to contributions from <a href="https://github.com/sponsors/alexclippinger">@alexclippinger</a>, we’ve added cleaned EIA923 Schedule 8B Financial Information to the PUDL database as <a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#i-core-eia923-yearly-byproduct-expenses-and-revenues"><span>_core_eia923__yearly_byproduct_expenses_and_revenues</span></a>. Once harvested, this table will be replaced with a well-normalized version of the same data, but it is being published in this form until then. See <a href="https://github.com/catalyst-cooperative/pudl/issues/4099">#4099</a> and <a href="https://github.com/catalyst-cooperative/pudl/issues/2448">#2448</a>, and <a href="https://github.com/catalyst-cooperative/pudl/pull/4636">#4636</a>.</p> </li> </ul> <h3>Documentation</h3> <ul> <li> <p>Added data source pages for:</p> <ul> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_sources/censuspep.html"><span>Population Estimates Program's (PEP) Federal Information Processing Series (FIPS) Codes</span></a>; see issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4375">#4375</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4622">#4622</a>.</p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_sources/sec10k.html"><span>U.S. Securities and Exchange Commission (SEC) Form 10-K</span></a>; see issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4329">#4329</a>, <a href="https://github.com/catalyst-cooperative/pudl/issues/4347">#4347</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4562">#4562</a>.</p> </li> </ul> </li> </ul> <h3>New Data Tests & Data Validations</h3> <ul> <li> <p>After investigating some modest discrepancies between our imputed hourly electricity demand and prior work by <a href="https://github.com/sponsors/truggles">@truggles</a> & <a href="https://github.com/sponsors/awongel">@awongel</a>, we’re removing the “EXPERIMENTAL” warning label that we had on those tables. See <a href="https://github.com/catalyst-cooperative/pudl-examples/pull/10">our discussion about the imputation results in the PUDL Examples repo</a>. The <a href="https://www.kaggle.com/code/catalystcooperative/06-pudl-imputed-electricity-demand">associated notebook is available on Kaggle</a></p> <p>This relates to the PUDL imputed demand values in following tables:</p> <ul> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-operations"><span>out_eia930__hourly_operations</span></a></p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-subregion-demand"><span>out_eia930__hourly_subregion_demand</span></a></p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-aggregated-demand"><span>out_eia930__hourly_aggregated_demand</span></a></p> </li> </ul> </li> </ul> <h3>Deprecations</h3> <ul> <li> <p>We have finally shut down our long-suffering <a href="https://datasette.io">Datasette</a> deployment, but are still working on achieiving feature parity in the new <a href="https://viewer.catalyst.coop">PUDL Data Viewer</a>. We have <a href="https://github.com/catalyst-cooperative/eel-hole/issues/36">an epic tracking our progress</a>. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4481">#4481</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4605">#4605</a> for the removal of Datasette references within the main PUDL repo.</p> </li> </ul> <h2><strong>Other PUDL v2025.10.0 Resources</strong></h2> <ul> <li><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html">PUDL v2025.10.0 Data Dictionary</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/">PUDL v2025.10.0 Documentation</a></li> <li><a href="https://registry.opendata.aws/catalyst-cooperative-pudl/">PUDL in the AWS Open Data Registry</a></li> <li>PUDL v2025.9.1 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2025.10.0/</li> <li>PUDL v2025.9.1 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2025.10.0/</li> <li><a href="https://doi.org/10.5281/zenodo.17352325">Zenodo archive of the PUDL GitHub repo for this release</a></li> <li><a href="https://github.com/catalyst-cooperative/pudl/releases/tag/v2025.10.0">PUDL v2025.10.0 release on GitHub</a></li> <li><a href="https://pypi.org/project/catalystcoop.pudl/2025.10.0">PUDL v2025.10.0 package in the Python Package Index (PyPI)</a></li> </ul> <h2><strong>Contact Us</strong></h2> <p><strong>If you're using PUDL, we would love to hear from you!</strong> Even if it's just a note to let us know that you exist, and how you're using the software or data. Here's a bunch of different ways to get in touch:</p> <ul> <li><a href="https://github.com/catalyst-cooperative">Follow us on GitHub</a></li> <li>Use the <a href="https://github.com/catalyst-cooperative/pudl/issues">PUDL Github issue tracker</a> to let us know about any bugs or data issues you encounter</li> <li><a href="https://github.com/orgs/catalyst-cooperative/discussions">GitHub Discussions</a> is where we provide user support.</li> <li>Watch our <a href="https://github.com/orgs/catalyst-cooperative/projects/9">GitHub Project</a> to see what we're working on.</li> <li>Email us at <a href="mailto:hello@catalyst.coop">hello@catalyst.coop</a> for private communications.</li> <li>On Mastodon: <a href="https://mastodon.energy/@catalystcoop">@CatalystCoop@mastodon.energy</a></li> <li>On BlueSky: <a href="https://bsky.app/profile/catalyst.coop">@catalyst.coop</a></li> <li>On Twitter: <a href="https://twitter.com/CatalystCoop">@CatalystCoop</a></li> <li>Connect with us <a href="https://www.linkedin.com/company/catalyst-cooperative/">on LinkedIn</a></li> <li>Play with our data and notebooks <a href="https://www.kaggle.com/catalystcooperative">on Kaggle</a></li> <li>Combine our data with ML models <a href="https://huggingface.co/catalystcooperative">on HuggingFace</a></li> <li>Learn more about us on our website: <a href="https://catalyst.coop">https://catalyst.coop</a></li> <li>Subscribe to our announcements list for <a href="https://catalyst.coop/updates">email updates</a>.</li> </ul>
Dataset from: Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications
<p>This record includes the data associated with the study "Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications". In this study, we evaluated the extent and quality of patient involvement reporting in examples of current practice in health research. We used a targeted search strategy to identify publications that report on patient involvement using the following three samples:</p> <ul> <li>Publications published in 2019 in <em>The BMJ, </em>which requires reporting on patient involvement in research articles</li> <li>Publications listed in the PCORI database. We filtered for topic: example of engagement in health research; stakeholder involvement: patients; year: 2019</li> <li>Publications citing the <a href="https://www.bmj.com/content/358/bmj.j3453">GRIPP2 reporting checklist</a> or a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1369-7625.2010.00607.x">critical appraisal guideline to assess the quality and impact of user involvement in research</a></li> </ul> <p>After applying our inclusion and exclusion criteria, the final sample consisted of 87 publications that reported on patient involvement. Included publications were coded according to three coding schemes.</p> <p>This deposit includes the following:</p> <ul> <li>Overview of publications that did not meet our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Overview of publications and additional documents (if applicable) that met our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Coded segments and analysis for the coding scheme "Phase of involvement" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table. </li> <li>Coded segments and analysis for the coding scheme "GRIPP2-SF reporting guidelines according to Staniszewska et al., 2017" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> <li>Coded segments and analysis for the coding scheme "Critical appraisal tool according to Wright et al., (2010)" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> </ul>
Literature search for publication: A meta-analysis on the role older adults with cancer favour in treatment decision making
<p>Dataset belonging to 10.5281/zenodo.7308287 including data of literature search regardign outcome preference scale in geriatric oncology</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Data used in the "H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra" publication
<p>Dynamic spectra used in the "A new approach to phase retrieval for pulsar dynamic spectra" publication. These data can be used to reproduce the results in the paper</p>
Dataset for publication "Cell design strategies for sodium-zinc chloride (Na-ZnCl2) batteries, and first demonstration of tubular cells with 38 Ah capacity"
<p><span lang="EN-US">stationary energy storage; ZEBRA battery; high-temperature metal chloride battery; molten-salt battery; molten sodium anode.</span></p> <p>Measured data to recreate Figures 1-8 in the above manuscript.</p>
Dataset supporting publication: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."
<p>DATASET suporting: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."</p> <p>Piccinini, Alessandro; Hajdukiewicz, Magdalena; D'Angelo, Letizia; Blanes, Luis Miguel; Keane, Marcus M.</p> <p>This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a ResistorCapacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.</p>
Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>
Pre-Publication Dataset: Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations
<p>These are the underlying data and do file to support the analysis in the forthcoming paper "Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations" that has been submitted to the <em>Data & Policy </em>Journal. This is an expanded and updated version of the Data For Policy conference paper "Comparing Measures of Internet Censorship: Analyzing the Tradeoffs between Expert Analysis and Remote Measurement" (10.5281/zenodo.3967398).</p>
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Supplementary Materials to the publication Wood structure explained by complex spatial source-sink interactions
<p>Model output and visualisation scripts to the publication Wood structure explained by complex spatial source-sink interactions. A readme explains the file origin. model output files and their variables and units are described within the .R analysis code.</p>
Data generated for the publication of Xie,S., Valente,L., &Etienne,R.S
<p>This repository shows the data for the publication of Xie,S., Valente,L., &Etienne,R.S. Can we ignore trait-dependent colonization and diversification in island biogeography?</p> <p>All files were obtained via computation at University of Groningen Peregrine High Performance Computing Cluster (HPCC).<br> We use the R package DAISIErobustness and the R package DAISIE to generate the data which was analyzed in the paper. The code for these packages is version controlled on GitHub and is freely available in open-source repositories. See the Related Identifiers section for links to relevant archived versions of both these packages.</p>
HETDEX Public Source Catalog 1: 220 K Sources including over 50K Lyman Alpha Emitters From an Untargeted Wide-area Spectroscopic Survey
<p>We present the first publicly released catalog of sources obtained from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). HETDEX is an integral field spectroscopic survey designed to measure the Hubble expansion parameter and angular diameter distance at 1.88 < z < 3.52 by using the spatial distribution of more than a million Lyα-emitting galaxies over a total target area of 540 deg2. The catalog comes from contiguous fiber spectra coverage of 25 deg2 of sky from January 2017 through June 2020, where object detection is performed through two complementary detection methods: one designed to search for line emission and the other a search for continuum emission. The HETDEX public release catalog is dominated by emission-line galaxies and includes 51,863 Lyα-emitting galaxy (LAE) identifications and 123,891 [O II]-emitting galaxies at z < 0.5. Also included in the catalog are 37,916 stars, 5,274 low-redshift (z < 0.5) galaxies without emission lines, and 4,976 active galactic nuclei. The catalog provides sky coordinates, redshifts, line identifications, classification information, line fluxes, [O II] and Lyα line luminosities where applicable, and spectra for all identified sources processed by the HETDEX detection pipeline. Extensive testing demonstrates that HETDEX redshifts agree to within ∆z < 0.02, 96.1% of the time to those in external spectroscopic catalogs. We measure the photometric counterpart fraction in deep ancillary Hyper Suprime-Cam imaging and find that only 55.5% of the LAE sample has an r-band continuum counterpart down to a limiting magnitude of r ∼ 26.2 mag (AB) indicating that an LAE search of similar sensitivity with photometric pre-selection would miss nearly half of the HETDEX LAE catalog sample.<br> <br> Two catalogs make up HETDEX Source Catalog 1:<br> <br> 1. The Source Observation Table: hetdex_sc1_vX.dat/.fits/.ecsv<br> With SPECTRA arrays included: hetdex_sc1_spec_vX.fits<br> <br> One row per source observation. The table provides basic coordinates/redshift/source information for each observation of a unique astornomical source. The larger file hetdex_sc1_spec_vX.fits contains the same info from the first table plus addition data units of spectral array data.<br> <br> 2. The Detection Information Table: hetdex_sc1_detinfo_vX.fits<br> One row per line or continuum detection. Bright sources can be comprised of multiple line or continuum emission. This catalog provides specific detection information such as line parameter info (S/N, line flux, line width)<br> <br> 3. Jupyter Notebook with access example: HETDEX_source_catalog_1.ipynb/.pdf/.html</p> <p>We request that the following acknowledgement be included in any paper using data or software from HETDEX public data releases:</p> <blockquote> <p>HETDEX is led by the University of Texas at Austin McDonald Observatory and Department of Astronomy with participation from the Ludwig-Maximilians-Universität München, Max-Planck-Institut für Extraterrestrische Physik (MPE), Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Pennsylvania State University, Institut für Astrophysik Göttingen, The University of Oxford, Max-Planck-Institut für Astrophysik (MPA), The University of Tokyo and Missouri University of Science and Technology.</p> <p>Observations for HETDEX were obtained with the Hobby-Eberly Telescope (HET), which is a joint project of the University of Texas at Austin, the Pennsylvania State University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen. The HET is named in honor of its principal benefactors, William P. Hobby and Robert E. Eberly. The Visible Integral-field Replicable Unit Spectrograph (VIRUS) was used for HETDEX observations. VIRUS is a joint project of the University of Texas at Austin, Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Max-Planck-Institut fürExtraterrestrische Physik (MPE), Ludwig-Maximilians-Universität München, Pennsylvania State University, Institut für Astrophysik Göttingen, University of Oxford, and the Max-Planck-Institut fur Astrophysik (MPA).</p> <p>Funding for HETDEX has been provided by the partner institutions, the National Science Foundation, the State of Texas, the US Air Force, and by generous support from private individuals and foundations.</p> </blockquote> <p> </p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".
<p><strong>Publication: </strong>Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.</p> <p> </p> <p><strong>Abstract: </strong> Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.</p> <p> </p> <p><strong>This record contains two separate mass-spectrometry metabolomics data sets associated with this study:</strong></p> <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> <p>For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.</p> <p> </p> <p><strong>Data sets in other repositories associated with the same study:</strong></p> <p>Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories:</p> <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul> <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.