cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jbwgoo
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year 1950-2019",
 "TaxonKey is Vieja guttulata (Günther, 1864)"
 ]
}

 The dataset includes 127 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011514-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjewtq
title: Occurrence Download

description: A dataset containing 3078 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cenchrus echinatus L.",
 "Year 1981-2015"
 ]
}

 The dataset includes 3078 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059535-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b2k2zt
title: Occurrence Download

description: A dataset containing 287741 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Araschnia levana (Linnaeus, 1758)",
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 287741 records from 952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059540-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15633036
title: QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.1.0

description: A quantum chemical (QC) dataset of optimization targets was generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train the parameters of the OpenFF 2.1.0 Sage forcefield.

 

Additional details can be found in the GitHub dataset repository and the Force field repository.



 

General Information


* Date: 2025-05-22

* Purpose: Complete set of training data for OpenFF 2.1.0 Sage

* Name: OpenFF SMIRNOFF Sage 2.1.0

* Submitter: Jennifer A Clark

* Dataset curator: Pavan Behara

 

* Class: OpenFF Optimization Dataset

* Dataset Type: optimization

* Number of unique molecules: 1701

* Number of filtered molecules: 0

* Number of conformers: 5580

* Number of conformers (min, mean, max): 1.00, 3.17, 17.00

* Molecular weight (min, mean, max): 16.04, 235.40, 544.64

* Charges: -3.0, -2.0, -1.0, 0.0, 1.0, 2.0

* Dataset generator: Chaya Stern, Hyesu Jang, Jessica Maat, and Pavan Behara

 

* Class: OpenFF TorsionDrive Dataset

* Number of unique molecules: 953

* Number of filtered molecules: 0

* Number of driven torsions: 1300

* Number of conformers: 974

* Number of conformers (min, mean, max): 1, 1, 3

* Molecular weight (min, mean, max): 32.04, 185.54, 503.42

* Charges: -1.0, 0.0, 1.0

* Dataset generator: Simon Boothroyd, John Chodera, Trevor Gokey, Hyesu Jang, Yudong Qiu, Bryon Tjanaka
description: Manifest





OpenFF-SMIRNOFF-Sage-2.1.0_optimization_view.sqlite : QCFractal view of optimization dataset 446

OpenFF-SMIRNOFF-Sage-2.1.0_torsiondrive_view.sqlite : QCFractal view of torsiondrive dataset 445

docker_handle_dataset_views.tar.gz : Compressed docker image providing a notebook ready to view and handle the data

README.txt on how to launch the docker image and access the data





Instructions for Dataset Handling

This data is stored a an sqlite file the is directly readable with QCFractal as a dataset view. Because sqlite is a general format, this data may also be accessed in that way, where compressed contents are python picked json strings. To make viewing and handling the data straighforward, a docker image wiith a jupyter notebook entry has been provided.

Load the docker image with:

$ docker load -i docker_handle_dataset_views.tar.gz

A user can run the docker image to spawn the jupyter notebook.



$ mkdir views; mv *sqlite views/$ mkdir outputs$ docker run -p 8888:8888 -v ./views:/workspace/views -v ./outputs:/workspace/outputs docker_handle_dataset_views


The -p flag exposed the port 8888 inside the docker image to the port by the same name externally.The -v flag exposes a directory (in this case ./views, so put your dataset views there) to a directory inside the docker image so that the jupyter notebook and access them. The ./outputs directory provides another shared directory that can be useful to pass output files.If using a M* MAC, the flag --platform=linux/amd64 could be required.

Entering the URL that starts with http://127.0.0.1:8888... in a internet browser should lead to a jupyterlab instance.
DOI: 10.5281/zenodo.15633037
title: QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.1.0

description: A quantum chemical (QC) dataset of optimization targets was generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train the parameters of the OpenFF 2.1.0 Sage forcefield.

 

Additional details can be found in the GitHub dataset repository and the Force field repository.



 

General Information


* Date: 2025-05-22

* Purpose: Complete set of training data for OpenFF 2.1.0 Sage

* Name: OpenFF SMIRNOFF Sage 2.1.0

* Submitter: Jennifer A Clark

* Dataset curator: Pavan Behara

 

* Class: OpenFF Optimization Dataset

* Dataset Type: optimization

* Number of unique molecules: 1701

* Number of filtered molecules: 0

* Number of conformers: 5580

* Number of conformers (min, mean, max): 1.00, 3.17, 17.00

* Molecular weight (min, mean, max): 16.04, 235.40, 544.64

* Charges: -3.0, -2.0, -1.0, 0.0, 1.0, 2.0

* Dataset generator: Chaya Stern, Hyesu Jang, Jessica Maat, and Pavan Behara

 

* Class: OpenFF TorsionDrive Dataset

* Number of unique molecules: 953

* Number of filtered molecules: 0

* Number of driven torsions: 1300

* Number of conformers: 974

* Number of conformers (min, mean, max): 1, 1, 3

* Molecular weight (min, mean, max): 32.04, 185.54, 503.42

* Charges: -1.0, 0.0, 1.0

* Dataset generator: Simon Boothroyd, John Chodera, Trevor Gokey, Hyesu Jang, Yudong Qiu, Bryon Tjanaka
description: Manifest





OpenFF-SMIRNOFF-Sage-2.1.0_optimization_view.sqlite : QCFractal view of optimization dataset 446

OpenFF-SMIRNOFF-Sage-2.1.0_torsiondrive_view.sqlite : QCFractal view of torsiondrive dataset 445

docker_handle_dataset_views.tar.gz : Compressed docker image providing a notebook ready to view and handle the data

README.txt on how to launch the docker image and access the data





Instructions for Dataset Handling

This data is stored a an sqlite file the is directly readable with QCFractal as a dataset view. Because sqlite is a general format, this data may also be accessed in that way, where compressed contents are python picked json strings. To make viewing and handling the data straighforward, a docker image wiith a jupyter notebook entry has been provided.

Load the docker image with:

$ docker load -i docker_handle_dataset_views.tar.gz

A user can run the docker image to spawn the jupyter notebook.



$ mkdir views; mv *sqlite views/$ mkdir outputs$ docker run -p 8888:8888 -v ./views:/workspace/views -v ./outputs:/workspace/outputs docker_handle_dataset_views


The -p flag exposed the port 8888 inside the docker image to the port by the same name externally.The -v flag exposes a directory (in this case ./views, so put your dataset views there) to a directory inside the docker image so that the jupyter notebook and access them. The ./outputs directory provides another shared directory that can be useful to pass output files.If using a M* MAC, the flag --platform=linux/amd64 could be required.

Entering the URL that starts with http://127.0.0.1:8888... in a internet browser should lead to a jupyterlab instance.
DOI: 10.5281/zenodo.15745301
title: Information-optimal mixing at low Reynolds number [data set]

description: Jupyter notebook with Python code to perform numerical optimisation of mutual information for planar shear flow problems and compare performance of random protocols with theoretical bounds. Figs.2 and 3 of the relevant publication can be reproduced by running the code cell by cell in order of appearance. Datapoints of theoretical bounds for pure and simple shear are provided as separate .npy files.

See publication:

Information-optimal mixing at low Reynolds numberLuca Cocconi, Yihong Shi, Andrej Vilfan

https://doi.org/10.1103/cl9m-cmhb
DOI: 10.5281/zenodo.15745302
title: Information-optimal mixing at low Reynolds number [data set]

description: Jupyter notebook with Python code to perform numerical optimisation of mutual information for planar shear flow problems and compare performance of random protocols with theoretical bounds. Figs.2 and 3 of the relevant publication can be reproduced by running the code cell by cell in order of appearance. Datapoints of theoretical bounds for pure and simple shear are provided as separate .npy files.

See publication:

Information-optimal mixing at low Reynolds numberLuca Cocconi, Yihong Shi, Andrej Vilfan

https://doi.org/10.1103/cl9m-cmhb
DOI: 10.15156/bio/sh0209602.10fu
title: SH0209602.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209603.10fu
title: SH0209603.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209604.10fu
title: SH0209604.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209605.10fu
title: SH0209605.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209606.10fu
title: SH0209606.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.18203728
title: Martyn et al. (2025) Variant-EFFECTS Evaluator for GAME API

description: This Evaluator requests expression predictions for sequences in THP-1 monocytes and Jurkat T cells. It then computes the log2 fold change (log2FC) between alternate and reference sequence predictions, and evaluates performance by calculating the Pearson correlation between the measure



Included in Engreitz_evaluator.sif :



Scripts to process the data and connect to predictors in the GAME API

Scripts to parse the returned predictions and calculate performance metrics

All software dependencies


/evaluator_data folder contents:



/Jurkat



all_jurkat_sequences.tsv: SPDI ID; Reference Sequence; Alternate Sequence. ~2000 bp long sequence (depending on indel size)

all_Jurkat.tsv: concatenated variants from individual variant files in SPDI format. 332 total variants.



/THP1



all_THP1_sequences.tsv: SPDI ID; Reference Sequence; Alternate Sequences. ~2000 bp long sequence (depending on indel size)

all_THP1.tsv : concatenated variants from individual variant files in SPDI format. 392 total variants.



/SPDI_toseq



instructions.tsv : How to run the Rscript to pull sequences from SPDI IDs (hg38)

.yaml to create the conda enviroment for the Rscript 



parse_Engreitz_data.py

File Specification_VariantEffectsFiles.png: Details about information in the variant files


How to run:

apptainer run --containall -B /path_to/evaluator_data/:/evaluator_data -B /path_to/prediction_folder/:/predictions Engreitz_evaluator.sif HOST PORT /predictions

Notes:



The main evaluator script (Engreitz_evaluator.py)will read in the *_sequences.tsv files from their respective folders and send two seperate requests to the connected Predictor

Duplicated SPDI ID sequences are only sent to the Predictors once to minimize duplicated computation and the values are merged with the measured values to calculate the correlation



Additional information can be found on GitHub: Genomic API for Model Evaluation

Original Publication can be found here: Martyn et al. (2025)
DOI: 10.15156/bio/sh0209607.10fu
title: SH0209607.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.16914393
title: Martyn et al. (2025) Variant-EFFECTS Evaluator for GAME API

description: This Evaluator requests expression predictions for sequences in THP-1 monocytes and Jurkat T cells. It then computes the log2 fold change (log2FC) between alternate and reference sequence predictions, and evaluates performance by calculating the Pearson correlation between the measure



Included in Engreitz_evaluator.sif :



Scripts to process the data and connect to predictors in the GAME API

Scripts to parse the returned predictions and calculate performance metrics

All software dependencies


/evaluator_data folder contents:



/Jurkat



all_jurkat_sequences.tsv: SPDI ID; Reference Sequence; Alternate Sequence. ~2000 bp long sequence (depending on indel size)

all_Jurkat.tsv: concatenated variants from individual variant files in SPDI format. 332 total variants.



/THP1



all_THP1_sequences.tsv: SPDI ID; Reference Sequence; Alternate Sequences. ~2000 bp long sequence (depending on indel size)

all_THP1.tsv : concatenated variants from individual variant files in SPDI format. 392 total variants.



/SPDI_toseq



instructions.tsv : How to run the Rscript to pull sequences from SPDI IDs (hg38)

.yaml to create the conda enviroment for the Rscript 



parse_Engreitz_data.py

File Specification_VariantEffectsFiles.png: Details about information in the variant files


How to run:

apptainer run --containall -B /path_to/evaluator_data/:/evaluator_data -B /path_to/prediction_folder/:/predictions Engreitz_evaluator.sif HOST PORT /predictions

Notes:



The main evaluator script (Engreitz_evaluator.py)will read in the *_sequences.tsv files from their respective folders and send two seperate requests to the connected Predictor

Duplicated SPDI ID sequences are only sent to the Predictors once to minimize duplicated computation and the values are merged with the measured values to calculate the correlation



Additional information can be found on GitHub: Genomic API for Model Evaluation

Original Publication can be found here: Martyn et al. (2025)
DOI: 10.15156/bio/sh0209608.10fu
title: SH0209608.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209609.10fu
title: SH0209609.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209610.10fu
title: SH0209610.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.