cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/inscriptifact-ouc110587130
title: Persepolis Fortification Aramaic Text 171, PFAT 171

description: [Text Description:] Administrative text with seal PFATS 0199 ❧ [Text or Publication Number Note:] PFAT 171, Bowman 171 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impression PFATS 0199 on reverse.
DOI: 10.25549/inscriptifact-ouc110587158
title: Persepolis Fortification Aramaic Text 172, PFAT 172

description: [Text Description:] Administrative text with seal PFATS 0205 ❧ [Text or Publication Number Note:] PFAT 172, Bowman 172 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impressions PFATS 0205 bottom edge, obverse. ❧ [Physical Object Note:] Round edge damaged
DOI: 10.25549/inscriptifact-ouc110587111
title: Persepolis Fortification Aramaic Text 171, PFAT 171

description: [Text Description:] Administrative text with seal PFATS 0199 ❧ [Text or Publication Number Note:] PFAT 171, Bowman 171 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impression PFATS 0199 on reverse.
DOI: 10.25549/inscriptifact-ouc110587152
title: Persepolis Fortification Aramaic Text 172, PFAT 172

description: [Text Description:] Administrative text with seal PFATS 0205 ❧ [Text or Publication Number Note:] PFAT 172, Bowman 172 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impressions PFATS 0205 bottom edge, obverse. ❧ [Physical Object Note:] Round edge damaged
DOI: 10.6084/m9.figshare.28919426
title: <b>Supplementary material for “Attitudes, perceived knowledge, and experiences of c</b><b>ondom use, STIs, transactional sex, </b><b>and sexual </b><b>healthcare among </b><b>young male forced migrants in Stockholm, Sweden: A qualitative study”</b>

description: In this study, we aimed to explore young male forced migrants' attitudes, perceived knowledge, and experiences regarding STIs, condom use transactional sex, and sexual health care utilization. A total of 32 semi-structured interviews were conducted with young male forced migrants in Stockholm, Sweden.
DOI: 10.6084/m9.figshare.28919426.v1
title: <b>Supplementary material for “Attitudes, perceived knowledge, and experiences of c</b><b>ondom use, STIs, transactional sex, </b><b>and sexual </b><b>healthcare among </b><b>young male forced migrants in Stockholm, Sweden: A qualitative study”</b>

description: In this study, we aimed to explore young male forced migrants' attitudes, perceived knowledge, and experiences regarding STIs, condom use transactional sex, and sexual health care utilization. A total of 32 semi-structured interviews were conducted with young male forced migrants in Stockholm, Sweden.
DOI: 10.21227/dez7-aj32
title: "Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction"

description: "The WESAD dataset is a widely used multimodal benchmark for wearable stress and affect detection. In this work, we present a processed and feature-engineered version of the WESAD dataset, specifically curated for emotion recognition and Electrodermal Activity (EDA) prediction tasks. The dataset includes synchronized and standardized input features extracted from raw physiological signals, including EDA, respiration, and body temperature, along with emotion labels mapped to discrete affective states.In addition to the original signals, we provide a suite of derived features such as statistical signal descriptors, frequency-domain representations, and smoothed temporal segments. These features are intended to support research on interpretable and multi-task deep learning frameworks, such as Physics-Informed Neural Networks (PINNs) for emotion-aware modeling.The dataset is cleaned, annotated, and formatted as CSV files, making it readily accessible for training, evaluation, and benchmarking of multimodal affective computing models. All preprocessing steps and label mappings are documented to ensure reproducibility."
DOI: 10.5281/zenodo.10275052
title: PUDL Data Release v2023.12.01

description: PUDL v2023.12.01 Data Release
This is a data release from the Public Utility Data Liberation (PUDL) project.  It's the first data-only release we've published. All of the tables which were previously only available by using the PUDL software package to process the data we previously published in the PUDL SQLite database are now being written into the database itself. This should make it easier for people to access with minimal setup, using a variety of different tools: Python, R, DuckDB, and many others! We are still committed to keeping the data processing pipeline behind this data free and open and transparent, we just don't want everyone to have to install and work with that software it if all they want is the output data!
We are about to do a major reorganization of the database, renaming almost every table and number of columns. This data release is a snapshot of the database before all that change happens, and is meant to provide continuity for users who are already working with the database, so that they can access to all the final 2022 data and migrate to the new database structure at a time of their own choosing over the coming months. We will do another data release soon, containing data through 2022, but with the new table and column names.
Other PUDL v2023.12.01 Resources

PUDL v2023.12.01 Release Notes
PUDL v2023.12.01 Data Dictionary
PUDL v2023.12.01 Documentation
PUDL v2023.12.01 on Kaggle (Corresponds to v9 of the PUDL Project dataset)
PUDL in the AWS Open Data Registry
PUDL v2023.12.01 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2023.12.01/
PUDL v2023.12.01 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2023.12.01/
PUDL v2023.12.01 Software Release
This is the software that was used to produce the data release. It is not necessary to work with the data, but it's linked here to provide transparency and provenance:

Zenodo archive of the GitHub repository
PUDL v2023.12.01 release tag on GitHub
PUDL v2023.12.1 package in the Python Package Index (PyPI)
Contact Us
If you're using PUDL, we would love to hear from you! 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:

Follow us on GitHub
Use the PUDL Github issue tracker to let us know about any bugs or data issues you encounter
GitHub Discussions is where we provide user support.
Watch our GitHub Project to see what we're working on.
Email us at hello@catalyst.coop for private communications.
On Mastodon: @CatalystCoop@mastodon.energy
On BlueSky: @catalyst.coop
On Twitter: @CatalystCoop
Play with our data and notebooks on Kaggle
Combine our data with ML models on HuggingFace
Learn more about us on our website: https://catalyst.coop
Subscribe to our announcements list for email updates.
DOI: 10.5281/zenodo.14169544
title: Public Utility Data Liberation Project (PUDL) Data Release

description: PUDL v2024.11.0 Data Release

PUDL v2024.11.0 is a regularly scheduled quarterly release, incorporating a few updates to the following datasets that have come out since the special release we did in October.



New Data Coverage


EIA 930







Added EIA 930 hourly data through the end of October as part of the Q3 quarterly release. See #3942 and #3946.






EIA 923








Added EIA 923 data from August 2024 as part of the Q3 quarterly release. See #3941 and PR #3950.






EIA 860M








Added 2024 EIA 860m data from August, September, and October as part of the Q3 quarterly release. See #3940 and PR #3949.






EIA Bulk Electricity Data








Updated the EIA Bulk Electricity data to include data published up through 2024-08-01. See #3944 and PR #3951.






EPA CEMS






Added 2024 Q3 of CEMS data. See #3943 and #3948.



Other PUDL v2024.10.0 Resources



PUDL v2024.11.0 Data Dictionary

PUDL v2024.11.0 Documentation

PUDL in the AWS Open Data Registry

PUDL v2024.11.0 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2024.11.0/

PUDL v2024.11.0 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2024.11.0/

Zenodo archive of the PUDL GitHub repo for this release

PUDL v2024.11.0 release on GitHub

PUDL v2024.11.0 package in the Python Package Index (PyPI)


Contact Us

If you're using PUDL, we would love to hear from you! 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:



Follow us on GitHub

Use the PUDL Github issue tracker to let us know about any bugs or data issues you encounter

GitHub Discussions is where we provide user support.

Watch our GitHub Project to see what we're working on.

Email us at hello@catalyst.coop for private communications.

On Mastodon: @CatalystCoop@mastodon.energy

On BlueSky: @catalyst.coop

On Twitter: @CatalystCoop

Connect with us on LinkedIn

Play with our data and notebooks on Kaggle

Combine our data with ML models on HuggingFace

Learn more about us on our website: https://catalyst.coop

Subscribe to our announcements list for email updates.
DOI: 10.5281/zenodo.5214231
title: PUDL Data Release v2.0.0

description: <strong>PUDL Data Release 2.0.0</strong> This is a data release from the Public Utility Data Liberation (PUDL) project. GitHub repository for the software used to generate this data. Zenodo archive of the particular version (v0.4.0) of the software that went into this release. For use in citations & long-term accessibility you can use this doi: https://doi.org/10.5281/zenodo.5207986 Documentation and release notes for the software and data. The software can be installed via the Python Package Index (PyPI) or from conda-forge. <strong>Using This Data</strong> The data in this archive is stored in a combination of SQLite database files, and Apache Parquet datasets. It can be used as a standalone resource, or in conjunction with the PUDL software. The PUDL documentation contains data dictionaries for many of the data tables. If you want to use the data in conjunction with the PUDL software, we've included a Docker image within the archive that will run a Jupyter Notebook Server containing examples of use based on our PUDL Examples repository. This Docker image contains all of the required software, and can access the associated archived data. Make sure that you've got Docker installed and running, and also have docker-compose. You'll want to allocate at least 8GB of memory to Docker. To use the Docker container to access and work with the data, download and extract the compressed tar archive on you computer. Inside the directory that is created when you extract the archive, you will find a Docker image. Load that image into your Docker environment locally with: <pre><code class="language-bash">docker load -i pudl-jupyter.tar</code></pre> Then within that same directory, run: <pre><code class="language-bash">docker-compose up</code></pre> This should start a Jupyter Notebook Server, and provide you with a link to connect to the server running on your local computer, beginning with <code>https://127.0.0.1:48512 or https://localhost:48512</code> You can select the tutorial notebooks from within the notebook interface. The README file contained in the archive and the PUDL Examples repository both provide more details on how to access and work with the data. <strong>Contact Us</strong> If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. You can also: Subscribe to our announcements list for email updates. Use the Github issue tracker to file bugs, suggest improvements, or ask for help. Email the project team at pudl@catalyst.coop for private communications. Follow @CatalystCoop on Twitter.
DOI: 10.5281/zenodo.5701406
title: PUDL Data Release v3.0.0

description: <strong>PUDL Data Release 3.0.0</strong> This is a data release from the Public Utility Data Liberation (PUDL) project. GitHub repository for the software used to generate this data. Zenodo archive of the particular version (v0.5.0) of the software that went into this release. For use in citations & long-term accessibility you can use this doi: https://doi.org/10.5281/zenodo.5677623 Documentation and release notes for the software and data. The software can be installed via the Python Package Index (PyPI) or from conda-forge. <strong>Using This Data</strong> The data in this archive is stored in a combination of SQLite database files, and Apache Parquet datasets. It can be used as a standalone resource, or in conjunction with the PUDL software. The PUDL documentation contains data dictionaries for many of the data tables. If you want to use the data in conjunction with the PUDL software, we've included a Docker image within the archive that will run a Jupyter Notebook Server containing examples of use based on our PUDL Examples repository. This Docker image contains all of the required software, and can access the associated archived data. Make sure that you've got Docker installed and running, and also have docker-compose. You'll want to allocate at least 8GB of memory to Docker. To use the Docker container to access and work with the data, download and extract the compressed tar archive on you computer. Inside the directory that is created when you extract the archive, you will find a Docker image. Load that image into your Docker environment locally with: <pre><code class="language-bash">docker load -i pudl-jupyter.tar</code></pre> Then within that same directory, run: <pre><code class="language-bash">docker-compose up</code></pre> This should start a Jupyter Notebook Server, and provide you with a link to connect to the server running on your local computer, beginning with <code>https://127.0.0.1:48512 or https://localhost:48512</code> You can select the tutorial notebooks from within the notebook interface. The README file contained in the archive and the PUDL Examples repository both provide more details on how to access and work with the data. <strong>Contact Us</strong> If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. You can also: Subscribe to our announcements list for email updates. Use the Github issue tracker to file bugs, suggest improvements, or ask for help. Email the project team at pudl@catalyst.coop for private communications. Follow @CatalystCoop on Twitter.
DOI: 10.5281/zenodo.6349861
title: PUDL Data Release v4.0.0

description: <strong>PUDL Data Release 4.0.0</strong> This is a data release from the Public Utility Data Liberation (PUDL) project. GitHub repository for the software used to generate this data. Zenodo archive of the particular version (v0.6.0) of the software that went into this release. For use in citations & long-term accessibility you can use this doi: https://doi.org/10.5281/zenodo.6349337 Documentation and release notes for the software and data. The software can be installed via the Python Package Index (PyPI) or from conda-forge. <strong>Using This Data</strong> The data in this archive is stored in a combination of SQLite database files, and Apache Parquet datasets. It can be used as a standalone resource, or in conjunction with the PUDL software. The PUDL documentation contains data dictionaries for many of the data tables. If you want to use the data in conjunction with the PUDL software, we've included a Docker image within the archive that will run a Jupyter Notebook Server containing examples of use based on our PUDL Examples repository. This Docker image contains all of the required software, and can access the associated archived data. Make sure that you've got Docker installed and running, and also have docker-compose. You'll want to allocate at least 8GB of memory to Docker. To use the Docker container to access and work with the data, download and extract the compressed tar archive on you computer. Inside the directory that is created when you extract the archive, you will find a Docker image. Load that image into your Docker environment locally with: <pre><code class="language-bash">docker load -i pudl-jupyter.tar</code></pre> Then within that same directory, run: <pre><code class="language-bash">docker-compose up</code></pre> This should start a Jupyter Notebook Server, and provide you with a link to connect to the server running on your local computer, beginning with <code>https://127.0.0.1:48512 or https://localhost:48512</code> You can select the tutorial notebooks from within the notebook interface. The README file contained in the archive and the PUDL Examples repository both provide more details on how to access and work with the data. <strong>Contact Us</strong> If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. You can also: Subscribe to our announcements list for email updates. Use the Github issue tracker to file bugs, suggest improvements, or ask for help. Email the project team at pudl@catalyst.coop for private communications. Follow @CatalystCoop on Twitter.
DOI: 10.5281/zenodo.7472137
title: PUDL Data Release v2022.11.30

description: <strong>PUDL Data Release v2022.11.30</strong> This is a data release from the Public Utility Data Liberation (PUDL) project. GitHub repository for the software used to generate this data. Zenodo archive of the particular version (v2022.11.30) of the software that went into this release. For use in citations & long-term accessibility you can use this doi: https://doi.org/10.5281/zenodo.6349337 Documentation and release notes for the software and data. The software can be installed via the Python Package Index (PyPI) or from conda-forge. <strong>Using This Data</strong> The data in this archive is stored in a combination of SQLite database files, and Apache Parquet datasets. It can be used as a standalone resource, or in conjunction with the PUDL software. The PUDL documentation contains data dictionaries for many of the data tables. If you want to use the data in conjunction with the PUDL software, we've included a Docker image within the archive that will run a Jupyter Notebook Server containing examples of use based on our PUDL Examples repository. This Docker image contains all of the required software, and can access the associated archived data. Make sure that you've got Docker installed and running, and also have docker-compose. You'll want to allocate at least 8GB of memory to Docker. To use the Docker container to access and work with the data, download and extract the compressed tar archive on you computer. Inside the directory that is created when you extract the archive, you will find a Docker image. Load that image into your Docker environment locally with: <pre><code class="language-bash">docker load -i pudl-jupyter.tar</code></pre> Then within that same directory, run: <pre><code class="language-bash">docker-compose up</code></pre> This should start a Jupyter Notebook Server, and provide you with a link to connect to the server running on your local computer, beginning with <code>https://127.0.0.1:48512 or https://localhost:48512</code> You can select the tutorial notebooks from within the notebook interface. The README file contained in the archive and the PUDL Examples repository both provide more details on how to access and work with the data. <strong>Contact Us</strong> If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. You can also: Subscribe to our announcements list for email updates. Use the Github issue tracker to file bugs, suggest improvements, or ask for help. Email the project team at pudl@catalyst.coop for private communications. Follow @CatalystCoop on Twitter.
DOI: 10.15468/dl.4v7r89
title: Occurrence Download

description: A dataset containing 7214 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cinnyris reichenowi Sharpe, 1891)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7214 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0299608-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.edvvgn
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "Country is Korea, Republic of",
 "TaxonKey is one of (Amaranthus spinosus L.)"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030889-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j97zz4
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aechmea kertesziae Reitz"
 ]
}

 The dataset includes 100 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001898-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.68a39v
title: Occurrence Download

description: A dataset containing 230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pitho aculeata (Gibbes, 1850)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 230 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0129555-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x5cuvg
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Terpsiphone smithii (Fraser, 1843)"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030890-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.