cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15480519
title: Abwassersurveillance AMELAG

description: Das Vorhaben "Abwassermonitoring für die epidemiologische Lagebewertung" (AMELAG) läuft vom 22.11.2022 bis zum 31.12.2025. Behörden, Kläranlagen und Labore arbeiten zusammen, um Proben zu nehmen, zu analysieren und zu bewerten. Das Ziel dieses Vorhabens ist es, SARS-CoV-2-Nachweise aus dem Abwasser als zusätzlichen Indikator zur epidemiologischen Lagebewertung auf Länder- und Bundesebene zu etablieren. Ebenso ist es das Ziel, Strukturen und Prozesse für ein bundesweites Netzwerk für die Abwassersurveillance weiter auszubauen, Konzepte für eine Verstetigung zu erstellen und die Möglichkeiten für ein Monitoring von weiteren Krankheitserregern im Abwasser zu erforschen.
Abwassersurveillance ist eine Technik, um Erreger im Abwasser nachzuweisen, um Gesundheitsschutzmaßnahmen besser steuern zu können. Abwasserdaten erlauben keine genaue Einschätzung von Krankheitsschwere oder der Belastung des Gesundheitssystems. Bei der epidemiologischen Bewertung sollten die Daten mit anderen Indikatoren, z.B. aus der syndromischen Surveillance, kombiniert werden.
DOI: 10.5281/zenodo.15534858
title: Abwassersurveillance AMELAG

description: Das Vorhaben "Abwassermonitoring für die epidemiologische Lagebewertung" (AMELAG) läuft vom 22.11.2022 bis zum 31.12.2025. Behörden, Kläranlagen und Labore arbeiten zusammen, um Proben zu nehmen, zu analysieren und zu bewerten. Das Ziel dieses Vorhabens ist es, SARS-CoV-2-Nachweise aus dem Abwasser als zusätzlichen Indikator zur epidemiologischen Lagebewertung auf Länder- und Bundesebene zu etablieren. Ebenso ist es das Ziel, Strukturen und Prozesse für ein bundesweites Netzwerk für die Abwassersurveillance weiter auszubauen, Konzepte für eine Verstetigung zu erstellen und die Möglichkeiten für ein Monitoring von weiteren Krankheitserregern im Abwasser zu erforschen.
Abwassersurveillance ist eine Technik, um Erreger im Abwasser nachzuweisen, um Gesundheitsschutzmaßnahmen besser steuern zu können. Abwasserdaten erlauben keine genaue Einschätzung von Krankheitsschwere oder der Belastung des Gesundheitssystems. Bei der epidemiologischen Bewertung sollten die Daten mit anderen Indikatoren, z.B. aus der syndromischen Surveillance, kombiniert werden.
DOI: 10.5281/zenodo.15592277
title: Abwassersurveillance AMELAG

description: Das Vorhaben "Abwassermonitoring für die epidemiologische Lagebewertung" (AMELAG) läuft vom 22.11.2022 bis zum 31.12.2025. Behörden, Kläranlagen und Labore arbeiten zusammen, um Proben zu nehmen, zu analysieren und zu bewerten. Das Ziel dieses Vorhabens ist es, SARS-CoV-2-Nachweise aus dem Abwasser als zusätzlichen Indikator zur epidemiologischen Lagebewertung auf Länder- und Bundesebene zu etablieren. Ebenso ist es das Ziel, Strukturen und Prozesse für ein bundesweites Netzwerk für die Abwassersurveillance weiter auszubauen, Konzepte für eine Verstetigung zu erstellen und die Möglichkeiten für ein Monitoring von weiteren Krankheitserregern im Abwasser zu erforschen.
Abwassersurveillance ist eine Technik, um Erreger im Abwasser nachzuweisen, um Gesundheitsschutzmaßnahmen besser steuern zu können. Abwasserdaten erlauben keine genaue Einschätzung von Krankheitsschwere oder der Belastung des Gesundheitssystems. Bei der epidemiologischen Bewertung sollten die Daten mit anderen Indikatoren, z.B. aus der syndromischen Surveillance, kombiniert werden.
DOI: 10.5281/zenodo.13254334
title: Abwassersurveillance AMELAG

description: Das Vorhaben "Abwassermonitoring für die epidemiologische Lagebewertung" (AMELAG) läuft vom 22.11.2022 bis zum 31.12.2024. Behörden, Kläranlagen und Labore arbeiten zusammen, um Proben zu nehmen, zu analysieren und zu bewerten. Das Ziel dieses Vorhabens ist es, SARS-CoV-2-Nachweise aus dem Abwasser als zusätzlichen Indikator zur epidemiologischen Lagebewertung auf Länder- und Bundesebene zu etablieren. Ebenso ist es das Ziel, Strukturen und Prozesse für ein bundesweites Netzwerk für die Abwassersurveillance weiter auszubauen, Konzepte für eine Verstetigung zu erstellen und die Möglichkeiten für ein Monitoring von weiteren Krankheitserregern im Abwasser zu erforschen.
Abwassersurveillance ist eine Technik, um Erreger im Abwasser nachzuweisen, um Gesundheitsschutzmaßnahmen besser steuern zu können. Abwasserdaten erlauben keine genaue Einschätzung von Krankheitsschwere oder der Belastung des Gesundheitssystems. Bei der epidemiologischen Bewertung sollten die Daten mit anderen Indikatoren, z.B. aus der syndromischen Surveillance, kombiniert werden.
DOI: 10.15468/dl.tan8kt
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Charadrius semipalmatus Bonaparte, 1825)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019905-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zn2q8a
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Anthophora senescens Lepeletier, 1841"
 ]
}

 The dataset includes 38 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019907-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15591049
title: METZE 3 –Phytoplankton abundance and biomass, Varna Lake, 2024

description: This dataset contains Phytoplankton abundance and biomass data collected  in Varna Lake (24-25.09.2024), , in the framework of H2020 DOORS Project. Taxonomic identification of phytoplankton cell counts were done under inverted microscope (Nikon Eclipse Ti, equipped with video-interactive image analysis system (Nis Elements) at 400 x magnification, according to the Black Sea region procedures described in Moncheva and Parr, 2010. Cell density was recalculated in cells/L and the biomass in mg/m3.
DOI: 10.5281/zenodo.15535272
title: Emiliania huxleyi abundance and biomass

description: This dataset contains Emiliania huxleyi abundance and biomass data collected during the  DOORS cruise #1  (01-20 May 2023), from 36 stations located in the Romanian Black Sea area, in the framework of H2020 DOORS Project. Taxonomic identification of Emiliania huxleyi and cell counts were done under inverted microscope (Nikon Eclipse TE2000, equipped with video-interactive image analysis system (L.U.C.I.A) at 400 x magnification, according to the Black Sea region procedures described in Moncheva and Parr, 2010. Cell density was recalculated in cells/L and the biomass in mg/m3 .
DOI: 10.5281/zenodo.15535273
title: Emiliania huxleyi abundance and biomass

description: This dataset contains Emiliania huxleyi abundance and biomass data collected during the  DOORS cruise #1  (01-20 May 2023), from 36 stations located in the Romanian Black Sea area, in the framework of H2020 DOORS Project. Taxonomic identification of Emiliania huxleyi and cell counts were done under inverted microscope (Nikon Eclipse TE2000, equipped with video-interactive image analysis system (L.U.C.I.A) at 400 x magnification, according to the Black Sea region procedures described in Moncheva and Parr, 2010. Cell density was recalculated in cells/L and the biomass in mg/m3 .
DOI: 10.15468/dl.zha6h2
title: Occurrence Download

description: A dataset containing 1601 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bionia coriacea (Nees) Benth."
 ]
}

 The dataset includes 1601 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019908-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmrqqa
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Blakea polyantha Wurdack"
 ]
}

 The dataset includes 44 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019909-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2njnzp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Chloris virgata",
 "Geometry POLYGON((29.76451 -26.09643, 32.1599 -26.09643, 32.1599 -22.51877, 29.76451 -22.51877, 29.76451 -26.09643))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019910-250525065834625/datasets/export for details.
DOI: 10.15468/dl.g8ruda
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Clutia affinis",
 "Geometry POLYGON((29.76451 -26.09643, 32.1599 -26.09643, 32.1599 -22.51877, 29.76451 -22.51877, 29.76451 -26.09643))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019911-250525065834625/datasets/export for details.
DOI: 10.15468/dl.yxjbyw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Crassula ovata",
 "Geometry POLYGON((29.76451 -26.09643, 32.1599 -26.09643, 32.1599 -22.51877, 29.76451 -22.51877, 29.76451 -26.09643))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019912-250525065834625/datasets/export for details.
DOI: 10.34740/kaggle/dsv/12057756
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.5281/zenodo.16910078
title: Trajectories used in: Unsupervised Density-Based Method for Analyzing Ion Mobility in Crystalline Solid-State Electrolytes

description: The datasets include CMD trajectories for the tetragonal and cubic LLZO systems (from LAMMPS), as well as AIMD trajectories for LGPS and both ordered and disordered LPSB systems (from VASP), all used in the evaluation of CrySF.
DOI: 10.5281/zenodo.16910079
title: Trajectories used in: Unsupervised Density-Based Method for Analyzing Ion Mobility in Crystalline Solid-State Electrolytes

description: The datasets include CMD trajectories for the tetragonal and cubic LLZO systems (from LAMMPS), as well as AIMD trajectories for LGPS and both ordered and disordered LPSB systems (from VASP), all used in the evaluation of CrySF.
DOI: 10.22002/qxbxa-gb145
title: Vibrio cholerae PM16

description: delta vc1403
description: <strong>Tilt Series Date:</strong> 2015-07-15</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O1 biovar El Tor</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>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> Simon Ringgaard</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-07-15-43/rawdata/</p>Vibriodelta1403doubleMCP0009.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/aba2015-07-15-43/rawdata/Vibriodelta1403doubleMCP0009.mrc"
        > <i class="download icon"></i> Download </a></p>

        Vibriodelta1403doubleMCP0009_full.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/aba2015-07-15-43/3dimage_59281/Vibriodelta1403doubleMCP0009_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/hnn24-rhg11
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/aba2015-07-09-60/rawdata/</p>A411V_53mag0025.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/aba2015-07-09-60/rawdata/A411V_53mag0025.mrc"
        > <i class="download icon"></i> Download </a></p>

        A411V_53mag0025_part41_15.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/aba2015-07-09-60/3dimage_59089/A411V_53mag0025_part41_15.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/27v93-fxg94
title: M. xanthus PilY1.3-sfGFP

description: PilY1.3-sfGFP (SA7661+pMAT321)Grid box YWC262
description: <strong>Tilt Series Date:</strong> 2017-11-27</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Myxococcus xanthus</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 form Lotte Sogaard-Andersen</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2017-11-27-112/rawdata/</p>20171127_YWC262_10115.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/ycw2017-11-27-112/rawdata/20171127_YWC262_10115.mrc"
        > <i class="download icon"></i> Download </a></p>

        20171127_YWC262_10115_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/ycw2017-11-27-112/3dimage_90755/20171127_YWC262_10115_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/0hb2w-nb737
title: Vibrio cholerae PM18

description: Vibro cholerae delta vca1092
description: <strong>Tilt Series Date:</strong> 2015-07-15</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O1 biovar El Tor</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>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> Simon Ringgaard</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2015-07-15-9/rawdata/</p>Vibriodelta1092July0011.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/aba2015-07-15-9/rawdata/Vibriodelta1092July0011.mrc"
        > <i class="download icon"></i> Download </a></p>

        Vibriodelta1092July0011_full.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/aba2015-07-15-9/3dimage_59214/Vibriodelta1092July0011_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.5281/zenodo.16755863
title: Benchmark Dataset for DevNous: An LLM-Based Multi-Agent System for Grounding IT Project Management in Unstructured Conversation

description: Overview

This dataset contains team conversation data with ground truth annotations for actionable intent tuples. The data were synthetically generated and annotated via human annotators.

Dataset Structure

The dataset contains 8 individual conversation files, each with 20 messages (160 total messages). Each conversation file represents a separate team conversation session.

Additionally the original initial state of tasks and team configuration that was used to generate the dataset is attached.

Data Fields

Each conversation file contains:



conversation_id: Unique identifier for the conversation

total_messages: Number of messages in this conversation

messages: Array of message entries, each containing:



conversation_id: Conversation identifier

message_index: Sequential index of the message within the conversation (0-19)

input_message:



user: Username of the person who sent the message

message: The actual text content of the message

time: Timestamp when the message was sent



ground_truth: Annotated classification in the format [category, action]




Classification Categories

The dataset uses the following classification scheme:

Message Categories:



NEW_TASK: Messages that introduce a new task or requirement

EXISTING_TASK: Messages related to ongoing tasks or work

WORKFLOW_RESPONSE: Messages that respond to or continue workflow processes

REGULAR_CONVERSATION: General team communication and social messages

SUMMARY_TRIGGER: Messages that request summaries or status updates

OTHER: Miscellaneous or unclear message types


Actions:



create_task: Creation of a new task or work item

update_context: Updates the context or information about existing work

continue_workflow: Continues or responds to ongoing workflow

generate_summary: Triggers summary generation

no_action: No specific action required


Citation

If you use this dataset in your research, please cite the original DevNous paper.

 

This dataset is provided for research and educational purposes.
DOI: 10.5281/zenodo.16755864
title: Benchmark Dataset for DevNous: An LLM-Based Multi-Agent System for Grounding IT Project Management in Unstructured Conversation

description: Overview

This dataset contains team conversation data with ground truth annotations for actionable intent tuples. The data were synthetically generated and annotated via human annotators.

Dataset Structure

The dataset contains 8 individual conversation files, each with 20 messages (160 total messages). Each conversation file represents a separate team conversation session.

Additionally the original initial state of tasks and team configuration that was used to generate the dataset is attached.

Data Fields

Each conversation file contains:



conversation_id: Unique identifier for the conversation

total_messages: Number of messages in this conversation

messages: Array of message entries, each containing:



conversation_id: Conversation identifier

message_index: Sequential index of the message within the conversation (0-19)

input_message:



user: Username of the person who sent the message

message: The actual text content of the message

time: Timestamp when the message was sent



ground_truth: Annotated classification in the format [category, action]




Classification Categories

The dataset uses the following classification scheme:

Message Categories:



NEW_TASK: Messages that introduce a new task or requirement

EXISTING_TASK: Messages related to ongoing tasks or work

WORKFLOW_RESPONSE: Messages that respond to or continue workflow processes

REGULAR_CONVERSATION: General team communication and social messages

SUMMARY_TRIGGER: Messages that request summaries or status updates

OTHER: Miscellaneous or unclear message types


Actions:



create_task: Creation of a new task or work item

update_context: Updates the context or information about existing work

continue_workflow: Continues or responds to ongoing workflow

generate_summary: Triggers summary generation

no_action: No specific action required


Citation

If you use this dataset in your research, please cite the original DevNous paper.

 

This dataset is provided for research and educational purposes.
DOI: 10.22002/3mcs0-p5k43
title: twitching Pseudomonas WT

description: Box YWC219-1Cells incubated on grid to promote twitching
description: <strong>Tilt Series Date:</strong> 2016-08-21</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: 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> Sample prepared in collaboration with Alex Persat</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2016-08-21-9/rawdata/</p>20160821_YWC219_20006.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/ycw2016-08-21-9/rawdata/20160821_YWC219_20006.mrc"
        > <i class="download icon"></i> Download </a></p>

        20160821_YWC219_20006_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/ycw2016-08-21-9/3dimage_78229/20160821_YWC219_20006_part81_7.rec"
        > <i class="download icon"></i> Download </a></p>