cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/cdde8-s2b40
title: Unspecified

description: Raw data files of Unspecified
description: <strong>Tilt Series Date:</strong> 2015-11-13</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Unspecified</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -8.0 μm,  magnification: 34000x. </p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Joe Falke</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Sample unknown; likely ΔcheA with fluorescently-labeled chemoreceptors</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-11-13-7/3dimage_70976/</p>LMEMPosQ1Amanual_part121_14.rec,  Reconstruction (Pixel Size 1.28 nm), 712.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-11-13-7/3dimage_70976/LMEMPosQ1Amanual_part121_14.rec"
        > <i class="download icon"></i> Download </a></p>

        LMEMPosQ1Amanual.mrc,  Tilt Series (Pixel Size 0.32 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-11-13-7/rawdata/LMEMPosQ1Amanual.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/w5px7czbn9.3
title: Ancient Kannada Handwritten Palm Leaf Dataset

description: The manuscripts are rare and invaluable pieces of history. We believe that by providing you with access to these texts, your research will contribute to a deeper understanding of the historical, cultural, and academic significance they hold.

Please ensure that all the manuscripts are handled with care and respect, in accordance with our guidelines, to preserve their integrity. We trust that your work will reflect the value of these documents, and we are eager to see the outcome of your research.

If you require any additional assistance or have any questions during your research, do not hesitate to contact us. We are happy to support you in any way possible.

Thank you for your interest in these important historical resources. We wish you all the best in your research endeavours.
DOI: 10.5281/zenodo.16947875
title: Traits of 15 Macroalgal Species from WoRMS and AlgaeTraits

description: This dataset compiles ecological and functional traits of 15 macroalgal species. Trait information was obtained from WoRMS (World Register of Marine Species) and AlgaeTraits, and processed through custom Python scripts. Following cleaning, normalization, and categorization, the dataset includes 21 traits such as asexual reproduction, spawning mode, life span, gametophyte arrangement, environmental position, body size, zonation, wave exposure, and seasonality. Trait harmonization steps included, for example, the standardization of body size into three classes: small, medium, and large. This curated dataset provides a harmonized resource for comparative analyses of macroalgal traits and serves as the foundation for integration with genomic data.
DOI: 10.5281/zenodo.16947876
title: Traits of 15 Macroalgal Species from WoRMS and AlgaeTraits

description: This dataset compiles ecological and functional traits of 15 macroalgal species. Trait information was obtained from WoRMS (World Register of Marine Species) and AlgaeTraits, and processed through custom Python scripts. Following cleaning, normalization, and categorization, the dataset includes 21 traits such as asexual reproduction, spawning mode, life span, gametophyte arrangement, environmental position, body size, zonation, wave exposure, and seasonality. Trait harmonization steps included, for example, the standardization of body size into three classes: small, medium, and large. This curated dataset provides a harmonized resource for comparative analyses of macroalgal traits and serves as the foundation for integration with genomic data.
DOI: 10.17632/xpr2yxmcnx
title: Regulatory Signal Dataset: FDA Accelerated Approval Documents

description: This dataset contains a collection of regulatory documents from the U.S. Food and Drug Administration (FDA) for drugs granted Accelerated Approval. It focuses on the modern regulatory review paradigm where information is distributed across multiple document types. This initial phase of data collection includes raw document files and their extracted text content for a broad set of applications, serving as a foundation for subsequent detailed signal extraction and analysis.

Description
This Phase 1 dataset comprises **224 application-level document sets** for drugs approved via the FDA's Accelerated Approval (AA) pathway. The application IDs were automatically extracted from the official FDA document "CDER Drug and Biologic Accelerated Approvals Based on a Surrogate Endpoint As of December 31, 2024".

Documents were then retrieved from the FDA's Drugs@FDA database, focusing on publicly available files.

Each document set is centered on a specific Application Number (e.g., BLA 761381) and contains the following key documents and their raw text content:

Document Types Included (Downloaded and Text Extracted):
* **Approval Letters:** Official regulatory decision documents (PDF).
* **Draft Labeling (USPI):** Product indication and efficacy claims (PDF).
* **Summary Review:** Often contains statistical information and context (CFM/HTML).
* **180-Day Progress Report:** Dedicated document for postmarketing requirement updates (CFM/HTML).

Extracted Information:
* **Raw Text Content:** For each downloaded document, its full text content is extracted and saved as a `.txt` file. This raw text serves as the primary input for subsequent, more sophisticated signal extraction.
* **Basic Statistical Signals (from combined text):** The current script performs a basic extraction of confidence intervals, p-values, and mentions of statistical methods from the combined text of all documents for an application. 

Purpose and Potential Applications
This dataset, in its current form, provides a robust foundation for research into automated regulatory intelligence. Its primary purpose is to enable the creation of a comprehensive, machine-readable corpus of FDA Accelerated Approval documents. This raw data is essential for:

* Developing Natural Language Processing (NLP) models to extract detailed regulatory signals.
* Training machine learning (ML) or quantum classification algorithms to predict Accelerated Approval outcomes.
* Analyzing the evolution of regulatory language and requirements over time.

The dataset is particularly valuable for researchers in clinical informatics, regulatory science, and health policy who seek to build transparent, auditable models for analyzing FDA decision-making.

Access and Usage
Data Format: The dataset is organized into a directory (`fda_documents_v2`) where each application's documents (PDFs, CFMs/HTMLs) and their corresponding extracted `.txt` files are stored. Extracted basic signals in `.json` files.
DOI: 10.22002/5t3wy-a7n57
title: Caulobacter SS347

description: Caulobacter crescentus strain SS347.deletion mutant causing a chaining phenotype
description: <strong>Tilt Series Date:</strong> 2015-01-08</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -10.0 μm,  magnification: 34000x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Martin Thanbichler</p>
<strong>Purification / Growth Conditions / Treatment:</strong> grown overnight in PYE</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-01-08-1/rawdata/</p>SS347manual1.mrc,  Tilt Series (Pixel Size 0.32 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-01-08-1/rawdata/SS347manual1.mrc"
        > <i class="download icon"></i> Download </a></p>

        SS347manual1_part121_46.rec,  Reconstruction (Pixel Size 1.28 nm), 712.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-01-08-1/3dimage_50550/SS347manual1_part121_46.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/xpr2yxmcnx.1
title: Regulatory Signal Dataset: FDA Accelerated Approval Documents

description: This dataset contains a collection of regulatory documents from the U.S. Food and Drug Administration (FDA) for drugs granted Accelerated Approval. It focuses on the modern regulatory review paradigm where information is distributed across multiple document types. This initial phase of data collection includes raw document files and their extracted text content for a broad set of applications, serving as a foundation for subsequent detailed signal extraction and analysis.

Description
This Phase 1 dataset comprises **224 application-level document sets** for drugs approved via the FDA's Accelerated Approval (AA) pathway. The application IDs were automatically extracted from the official FDA document "CDER Drug and Biologic Accelerated Approvals Based on a Surrogate Endpoint As of December 31, 2024".

Documents were then retrieved from the FDA's Drugs@FDA database, focusing on publicly available files.

Each document set is centered on a specific Application Number (e.g., BLA 761381) and contains the following key documents and their raw text content:

Document Types Included (Downloaded and Text Extracted):
* **Approval Letters:** Official regulatory decision documents (PDF).
* **Draft Labeling (USPI):** Product indication and efficacy claims (PDF).
* **Summary Review:** Often contains statistical information and context (CFM/HTML).
* **180-Day Progress Report:** Dedicated document for postmarketing requirement updates (CFM/HTML).

Extracted Information:
* **Raw Text Content:** For each downloaded document, its full text content is extracted and saved as a `.txt` file. This raw text serves as the primary input for subsequent, more sophisticated signal extraction.
* **Basic Statistical Signals (from combined text):** The current script performs a basic extraction of confidence intervals, p-values, and mentions of statistical methods from the combined text of all documents for an application. 

Purpose and Potential Applications
This dataset, in its current form, provides a robust foundation for research into automated regulatory intelligence. Its primary purpose is to enable the creation of a comprehensive, machine-readable corpus of FDA Accelerated Approval documents. This raw data is essential for:

* Developing Natural Language Processing (NLP) models to extract detailed regulatory signals.
* Training machine learning (ML) or quantum classification algorithms to predict Accelerated Approval outcomes.
* Analyzing the evolution of regulatory language and requirements over time.

The dataset is particularly valuable for researchers in clinical informatics, regulatory science, and health policy who seek to build transparent, auditable models for analyzing FDA decision-making.

Access and Usage
Data Format: The dataset is organized into a directory (`fda_documents_v2`) where each application's documents (PDFs, CFMs/HTMLs) and their corresponding extracted `.txt` files are stored. Extracted basic signals in `.json` files.
DOI: 10.22002/70e4f-rpw14
title: A411V in vitro

description: A411V PEG in vitro sample7.5%PEG (in 1x P.P buffer) with 50µM CFA411V4Q, 12µM CheA, 20 µM CheW, 4% Trehalose [50µl total volume]
description: <strong>Tilt Series Date:</strong> 2015-07-09</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Escherichia coli in vitro</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 80.0 eV/Å²,  defocus: -6.0 μm,  magnification: 53000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Lynmarie Thompson</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-07-09-8/rawdata/</p>A411V_53mag0001.mrc,  Tilt Series (Pixel Size 0.206 nm), 1.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-07-09-8/rawdata/A411V_53mag0001.mrc"
        > <i class="download icon"></i> Download </a></p>

        A411V_53mag0001_full.rec,  Reconstruction (Pixel Size 0.824 nm), 688.9 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-07-09-8/3dimage_58978/A411V_53mag0001_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/6qm37-ytg80
title: P. aeruginosa T6SS minicell

description: Pseudomonas JT1599Grid box YWC247
description: <strong>Tilt Series Date:</strong> 2017-04-14</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Pseudomonas aeruginosa</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.5°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain from Jonida Toska at John Mekalanos lab</p>
  ycw2017-04-14-91_slicer18063.jpg: Pseudomonas minicell
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-04-14-91/rawdata/</p>20170414_YWC247_10100.mrc,  Tilt Series (Pixel Size 0.39 nm), 2.3 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-04-14-91/rawdata/20170414_YWC247_10100.mrc"
        > <i class="download icon"></i> Download </a></p>

        20170414_YWC247_10100_part81_7.rec,  Reconstruction (Pixel Size 1.56 nm), 534.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-04-14-91/3dimage_83822/20170414_YWC247_10100_part81_7.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/31fy7-ptq08
title: Veillonella parvula

description: Raw data files of Veillonella parvula
description: <strong>Tilt Series Date:</strong> 2015-05-21</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Veillonella parvula</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -10.0 μm,  magnification: 18000x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Emma Allen Vercoe</p>
<strong>Purification / Growth Conditions / Treatment:</strong> clinical isolate</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-05-21-15/rawdata/</p>Veillonella0011.mrc,  Tilt Series (Pixel Size 0.63 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-05-21-15/rawdata/Veillonella0011.mrc"
        > <i class="download icon"></i> Download </a></p>

        Veillonella0011_full.rec,  Reconstruction (Pixel Size 2.52 nm), 534.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-05-21-15/3dimage_57363/Veillonella0011_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/mney8-er194
title: H. pylori 26695 WT

description: grid box YWC259
description: <strong>Tilt Series Date:</strong> 2017-07-06</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Helicobacter pylori</p>
<strong>Strain:</strong> 26695</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain and sample prepared by Carrie Shaffer</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-07-06-5/rawdata/</p>20170706_YWC259_10006.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-07-06-5/rawdata/20170706_YWC259_10006.mrc"
        > <i class="download icon"></i> Download </a></p>

        20170706_YWC259_10006.rec,  Reconstruction (Pixel Size 1.56 nm), 1.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-07-06-5/3dimage_85645/20170706_YWC259_10006.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/6mfyr-4c369
title: E. coli pED208_6um

description: Raw data files of E. coli pED208_6um
description: <strong>Tilt Series Date:</strong> 2015-01-17</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> cells were grown in LB o/n</p>
<strong>Sample Preparation:</strong> Vitrobot</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-01-17-31/rawdata/</p>ped208_6um0027.mrc,  Tilt Series (Pixel Size 0.5 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-01-17-31/rawdata/ped208_6um0027.mrc"
        > <i class="download icon"></i> Download </a></p>

        ped208_6um0027_part121_13.rec,  Reconstruction (Pixel Size 2.0 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2015-01-17-31/3dimage_50920/ped208_6um0027_part121_13.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/7rm05-nd386
title: NR1769/R1-16 conjugation

description: Grid box YWC231 grid3NR1769/R1-16 donor only control
description: <strong>Tilt Series Date:</strong> 2016-11-17</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain and sample prepared by Carrie Shaffer</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2016-11-17-4/3dimage_79767/</p>20161116_YWC231-3_10004_full.rec,  Reconstruction (Pixel Size 1.56 nm), 534.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2016-11-17-4/3dimage_79767/20161116_YWC231-3_10004_full.rec"
        > <i class="download icon"></i> Download </a></p>

        20161116_YWC231-3_10004.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2016-11-17-4/rawdata/20161116_YWC231-3_10004.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/gnf9khrbb9
title: Replication Files: Crime on the Move? The Effect of Ridesharing Services on Crime

description: The rapid rise of ridesharing services like Uber and Lyft has transformed urban transportation, but their influence on crime and the mechanisms driving these changes remain under explored. This study examines the impact of the introduction of ridesharing services like Uber and Lyft on crime rates across U.S. cities, leveraging a natural experiment created by their staggered rollout. Using a Two-way Fixed Effects (TWFE) model, I find that the introduction of ridesharing services leads to a 4.7% reduction in violent crimes, a 5.5% decrease in property crimes, and a 10.4% drop in burglary rates. No significant effects are observed for larceny, motor vehicle theft, or arson. I also investigate the heterogeneity of these effects across different demographic groups and conclude that the primary mechanism driving the crime reduction is the improvement in employment opportunities provided by ridesharing services.
DOI: 10.17632/gnf9khrbb9.2
title: Replication Files: Crime on the Move? The Effect of Ridesharing Services on Crime

description: The rapid rise of ridesharing services like Uber and Lyft has transformed urban transportation, but their influence on crime and the mechanisms driving these changes remain under explored. This study examines the impact of the introduction of ridesharing services like Uber and Lyft on crime rates across U.S. cities, leveraging a natural experiment created by their staggered rollout. Using a Two-way Fixed Effects (TWFE) model, I find that the introduction of ridesharing services leads to a 4.7% reduction in violent crimes, a 5.5% decrease in property crimes, and a 10.4% drop in burglary rates. No significant effects are observed for larceny, motor vehicle theft, or arson. I also investigate the heterogeneity of these effects across different demographic groups and conclude that the primary mechanism driving the crime reduction is the improvement in employment opportunities provided by ridesharing services.