cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18405147
title: Dataset EnzyBase12k: A Curated Dataset for Predicting Enzyme pH Optima Using pHoptNN

description: A Curated Dataset for Predicting Enzyme pH Optima

Overview

EnzyBase12k is a specialized dataset containing 11,615 monomeric enzyme structures mapped to their experimentally determined pH optima. This dataset was curated to facilitate the development of machine learning models, specifically Equivariant Graph Neural Networks (EGNNs), capable of predicting biochemical properties directly from 3D protein geometry.

The dataset was generated as part of the research:  “Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks” .

---

Dataset Structure

The dataset consists of two main parts:



Structural Files: 11,615 files in `.cif` format (Crystallographic Information Framework).

Metadata: `EnzyBase12k_metadata.csv` containing biological, structural, and experimental details.

Dataset Files: 11,615 files in `.pqr` and also in `.pdb`


 

Metadata Specification (`EnzyBase12k_metadata.csv`)

### Dataset Column Descriptions

| Column Name | Description | Possible Values / Notes || :--- | :--- | :--- || **`uniprot_id`** | Unique identifier for each enzyme from the UniProt database. | e.g., `P00789` || **`ph_optimum`** | The optimal pH level for enzymatic activity (retrieved from UniProt/BRENDA). | Numeric value. || **`structure_source`** | Indicates if the structure is experimental or predicted. | `PDB` (Experimental)<br>`AF` (De novo predicted) || **`structure_mode`** | Specifies the method used to obtain the structure. | `AF2_downloaded` (AlphaFold DB)<br>`AF3` (AlphaFold 3 prediction)<br>`AF3_template` (AF3 using templates) || **`pdb_id_final`** | The identifier of the best-matching experimental PDB structure (if available). | e.g., `1ABC` || **`pdb_chain`** | The specific protein chain identifier corresponding to `pdb_id_final`. | e.g., `A`, `B` || **`ec_id`** | The Enzyme Commission number classifying the enzymatic reaction. | e.g., `1.1.1.1` || **`seq_cut_to_domain`** | Binary flag indicating if the sequence was truncated to a functional domain. | `1` (Yes, cut)<br>`0` (No, full length) || **`systematic_name`** | The systematic name of the enzyme. | e.g., *Alcohol dehydrogenase* || **`organism`** | The scientific name of the source organism. | e.g., *Homo sapiens* || **`seq_length`** | The total number of amino acids in the sequence used. | Integer value. || **`uniprot_seq_cut`** | The actual amino acid sequence used (full or truncated). | String sequence. |

---

 Licensing

This dataset integrates data from multiple sources. Please adhere to the respective licenses:

UniProt Data: [CC BY 4.0](https://www.uniprot.org/help/license)PDB Structures: [CC0 1.0 Universal](https://rcsb.org)AlphaFold2 Models: [CC BY 4.0](https://alphafold.ebi.ac.uk/)AlphaFold3 Models: [CC BY-NC-SA 4.0](https://github.com/google-deepmind/alphafold3) (Non-Commercial, ShareAlike)

Metadata & Curation: The complete dataset curation and filtering process are done by Christian Clauß for the research project "Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks".

---}
DOI: 10.5281/zenodo.18405148
title: Dataset EnzyBase12k: A Curated Dataset for Predicting Enzyme pH Optima Using pHoptNN

description: A Curated Dataset for Predicting Enzyme pH Optima

Overview

EnzyBase12k is a specialized dataset containing 11,615 monomeric enzyme structures mapped to their experimentally determined pH optima. This dataset was curated to facilitate the development of machine learning models, specifically Equivariant Graph Neural Networks (EGNNs), capable of predicting biochemical properties directly from 3D protein geometry.

The dataset was generated as part of the research:  “Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks” .

---

Dataset Structure

The dataset consists of two main parts:



Structural Files: 11,615 files in `.cif` format (Crystallographic Information Framework).

Metadata: `EnzyBase12k_metadata.csv` containing biological, structural, and experimental details.

Dataset Files: 11,615 files in `.pqr` and also in `.pdb`


 

Metadata Specification (`EnzyBase12k_metadata.csv`)

### Dataset Column Descriptions

| Column Name | Description | Possible Values / Notes || :--- | :--- | :--- || **`uniprot_id`** | Unique identifier for each enzyme from the UniProt database. | e.g., `P00789` || **`ph_optimum`** | The optimal pH level for enzymatic activity (retrieved from UniProt/BRENDA). | Numeric value. || **`structure_source`** | Indicates if the structure is experimental or predicted. | `PDB` (Experimental)<br>`AF` (De novo predicted) || **`structure_mode`** | Specifies the method used to obtain the structure. | `AF2_downloaded` (AlphaFold DB)<br>`AF3` (AlphaFold 3 prediction)<br>`AF3_template` (AF3 using templates) || **`pdb_id_final`** | The identifier of the best-matching experimental PDB structure (if available). | e.g., `1ABC` || **`pdb_chain`** | The specific protein chain identifier corresponding to `pdb_id_final`. | e.g., `A`, `B` || **`ec_id`** | The Enzyme Commission number classifying the enzymatic reaction. | e.g., `1.1.1.1` || **`seq_cut_to_domain`** | Binary flag indicating if the sequence was truncated to a functional domain. | `1` (Yes, cut)<br>`0` (No, full length) || **`systematic_name`** | The systematic name of the enzyme. | e.g., *Alcohol dehydrogenase* || **`organism`** | The scientific name of the source organism. | e.g., *Homo sapiens* || **`seq_length`** | The total number of amino acids in the sequence used. | Integer value. || **`uniprot_seq_cut`** | The actual amino acid sequence used (full or truncated). | String sequence. |

---

 Licensing

This dataset integrates data from multiple sources. Please adhere to the respective licenses:

UniProt Data: [CC BY 4.0](https://www.uniprot.org/help/license)PDB Structures: [CC0 1.0 Universal](https://rcsb.org)AlphaFold2 Models: [CC BY 4.0](https://alphafold.ebi.ac.uk/)AlphaFold3 Models: [CC BY-NC-SA 4.0](https://github.com/google-deepmind/alphafold3) (Non-Commercial, ShareAlike)

Metadata & Curation: The complete dataset curation and filtering process are done by Christian Clauß for the research project "Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks".

---}
DOI: 10.5061/dryad.q573n5tr0
title: Data from: A model estimating the degree of floral transition in an evergreen perennial exposed to warm periods during winter

description: Increasing winter temperatures jeopardize the yield of fruit trees
 requiring a prolonged and sufficiently cold winter to flower. Assessing
 the exact risk to different crop varieties is the first step in mitigating
 the harmful effect of climate change. Here, we focus on olive (Olea
 europaea) – a traditional crop in the Mediterranean basin. Olive flowering
 depends on the sufficiency of cold periods and the lack of warm ones
 during the preceding winter. Yet, a satisfactory quantitative model
 forecasting its expected flowering under natural temperature conditions is
 still lacking. We empirically tested the effect of different temperature
 regimes on olive flowering intensity and flowering-gene expression. We
 constructed a dynamic model, describing the response of a putative
 flowering factor to the temperature signal. The crucial ingredient in the
 model is an unstable intermediate, produced and degraded at
 temperature-dependent rates. Our model accounts not only for the number of
 cold and warm hours but also for their order. We used empirical flowering
 and temperature data to fit the model parameters, applying numerical
 constrained optimization techniques, and successfully validated the model
 outcomes. Our model more accurately predicts flowering under winters with
 warm periods yielding low-to-moderate flowering and is more robust
 compared to previous models.
description: Hourly temperature data in the different experiment sites
 starting from 15/10 each autumn and ending at floral evocation (dates vary
 between the sites). 
description: # Data from: A model estimating the degree of floral transition in an
 evergreen perennial exposed to warm periods during winter
 [https://doi.org/10.5061/dryad.q573n5tr0](https://doi.org/10.5061/dryad.q573n5tr0) Hourly temperature data in the experimental sites in the relevant years starting from Oct 15 and ending at floral evocation (dates vary between sites). ## Description of the data and file structure The data is organized in columns. Each column has a title designating the name of the site and the year in which it was collected. For example: 'Reh 15-16' was collected in Rehovot in the winter of 2015-2016. Below the title there are 2 columns: the date and time in one column and the temperatures (degrees Celsius) in the other.
DOI: 10.15468/dl.srzpd9
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Brachyhypopomus beebei",
 "Year 2018-2026"
 ]
}

 The dataset includes 22 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003921-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gatr5m
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera uncia"
 ]
}

 The dataset includes 79 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003922-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2gxyth
title: Occurrence Download

description: A dataset containing 570 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is EN",
 "OccurrenceStatus is Present",
 "TaxonKey is Amazona viridigenalis",
 "Year 1990-2026"
 ]
}

 The dataset includes 570 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003924-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18406066
title: Supplementary Data from Riddell V et al.

description: Tab
Description
Code used to generate


1_sequencing_accessions
Metagenome and metatranscriptomes, accession numbers, and number of reads
NA


2_vOTU_manual_check_100
Genomad, CheckV, and DRAMv metadata for 100 randomly selected vOTUs, along with manual classification (first column)
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


3_filtered_out_vOTUs
Contigs flagged as vOTUs by Genomad, but removed based on lack of viral annotations and enrichment of host and transposon annotations
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


4_vOTU_metadata
Genomad, CheckV, and DRAMv annotations for each vOTU that passed manual filtering 
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


5_viral_taxonomy
vConTACT3 viral taxonomy for all manually filtered vOTUs
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/04-viral-taxonomy/scripts/run-vcontact3.sh


6_vOTU_MAG_predictions_no_cutoff
iPHoP MAG-level predictions for all 9,092 vOTUs that passed manual check. Activity status of vOTUs and MAGs are listed in the last few columns
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/03-predict-hosts/scripts/06-host-prediction-EDA.py


7_cytoscape_node_metadata
Metadata to reconstruct the vOTU-MAG network plot
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/03-predict-hosts/scripts/06-host-prediction-EDA.py


8_amg_summary_QC_with_host_pred
Genes flagged as AMGs from active vOTUs
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/11-get_AMGs_from_active_vOTUs.py


9_indicator_vOTUs
vOTUs considered indicators for at least one combination of sample clusters based on Indicator Species Analysis
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/02-get-relative-abundance/scripts/07-beta-diversity-and-ISA.R
DOI: 10.5281/zenodo.18406067
title: Supplementary Data from Riddell V et al.

description: Tab
Description
Code used to generate


1_sequencing_accessions
Metagenome and metatranscriptomes, accession numbers, and number of reads
NA


2_vOTU_manual_check_100
Genomad, CheckV, and DRAMv metadata for 100 randomly selected vOTUs, along with manual classification (first column)
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


3_filtered_out_vOTUs
Contigs flagged as vOTUs by Genomad, but removed based on lack of viral annotations and enrichment of host and transposon annotations
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


4_vOTU_metadata
Genomad, CheckV, and DRAMv annotations for each vOTU that passed manual filtering 
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/08-get_vOTU_metadata_and_filter.py


5_viral_taxonomy
vConTACT3 viral taxonomy for all manually filtered vOTUs
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/04-viral-taxonomy/scripts/run-vcontact3.sh


6_vOTU_MAG_predictions_no_cutoff
iPHoP MAG-level predictions for all 9,092 vOTUs that passed manual check. Activity status of vOTUs and MAGs are listed in the last few columns
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/03-predict-hosts/scripts/06-host-prediction-EDA.py


7_cytoscape_node_metadata
Metadata to reconstruct the vOTU-MAG network plot
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/03-predict-hosts/scripts/06-host-prediction-EDA.py


8_amg_summary_QC_with_host_pred
Genes flagged as AMGs from active vOTUs
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/01-build-vOTU-database/scripts/11-get_AMGs_from_active_vOTUs.py


9_indicator_vOTUs
vOTUs considered indicators for at least one combination of sample clusters based on Indicator Species Analysis
https://github.com/jamesriddellv/Grantham_Bioreactor/blob/main/02-get-relative-abundance/scripts/07-beta-diversity-and-ISA.R
DOI: 10.5061/dryad.dfn2z3595
title: Data from: Stimulus dependent emergence of understanding analogical relations in budgerigars

description: The ability for analogical reasoning, such as understanding that the
 relationship between two items is similar to the relationship between two
 other items, has long been considered to be unique to humans. Whether and
 to what extent non-human animals can recognize such relational analogies
 is still debated. We tested such abilities in two groups of budgerigars
 with a diverse set of item categories in which the figures of the stimuli
 were quite different in nature (size, color, shape, geometric type, and
 number). Budgerigars trained to discriminate two identical figures from
 the same two figures differing in size, generalized the discrimination to
 test stimuli belonging to the novel categories, demonstrating the
 spontaneous use of analogical relations. In contrast, budgerigars trained
 to discriminate two circles that were either the same or different in
 color seemed to generalize to novel stimuli by perceptual rather than
 conceptual similarity. The results thus demonstrate the spontaneous
 emergence of perceiving analogical relations in budgerigars of a nature
 beyond that demonstrated in previous studies of non-primate species.
 However, the results also show that whether budgerigars generalize a
 relationship by perceptual or conceptual similarity seems dependent on the
 nature of the training stimuli.
description: # Stimulus dependent emergence of understanding analogical relations in
 budgerigars The E1_size_training Data set and the E2_color_training Data
 set include the name of each bird in the two training sets, its sex, the
 number of the stimulus, the training unit, whether or not each bird chose
 correctly each time, and whether the selected stimulus was on the left or
 the right.
DOI: 10.15468/dl.b6jaff
title: Occurrence Download

description: A dataset containing 2557 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Charaxes bernardus"
 ]
}

 The dataset includes 2557 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003923-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qecf6j
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "CatalogNumber" : [
 "is one of (K-M000771102, K-M000771479, K-M000772199, K-M000773765, K-M000773865, K-M000773932, K-M000792615, K-M000817272, K-M000817273, K-M000818403, K-M000819353, K-M000819799, K-M000820309, K-M000820808, K-M000822505, K-M000822527, K-M000822722, K-M000822726, K-M000822728, K-M000822842, K-M000836457, K-M000836464, K-M000836466, K-M000868903, K-M000874863, K-M000897934, K-M000948317, K-M000949121, K-M000949144, K-M000951276, K-M000951317, K-M000951583, K-M000951755, K-M000951854, K-M000951855, K-M000954389, K-M000954750, K-M000955040, K-M000974224, K-M000977665, K-M000977970, K-M000979287, K-M000985086, K-M000985087, K-M001007897, K-M001043634, K-M001043637, K-M001043813, K-M001043815, K-M001043816, K-M001435724, K-M001441780, K-M001441830)"
 ]
}

 The dataset includes 49 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003926-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18022783
title: Single-nucleus RNA-seq Dataset of Neurons from the Popko Mouse Model

description: This repository contains processed single-nucleus RNA sequencing (snRNA-seq) data from neurons isolated from the Popko mouse model, provided as an AnnData (.h5ad) file. The dataset was generated to investigate how DNA damage burden and neuroinflammation contribute to selective loss of CUX2-expressing neurons. The data support the analyses presented in “DNA damage burden causes selective CUX2 neuron loss in neuroinflammation”.

The .h5ad file includes normalized gene expression matrices and accompanying metadata, such as sample identifiers, experimental conditions, and neuronal cell-type annotations. This dataset may be reused for studies of neuronal vulnerability, DNA damage responses, and neuroinflammation-related transcriptional changes.
DOI: 10.5281/zenodo.18022784
title: Single-nucleus RNA-seq Dataset of Neurons from the Popko Mouse Model

description: This repository contains processed single-nucleus RNA sequencing (snRNA-seq) data from neurons isolated from the Popko mouse model, provided as an AnnData (.h5ad) file. The dataset was generated to investigate how DNA damage burden and neuroinflammation contribute to selective loss of CUX2-expressing neurons. The data support the analyses presented in “DNA damage burden causes selective CUX2 neuron loss in neuroinflammation”.

The .h5ad file includes normalized gene expression matrices and accompanying metadata, such as sample identifiers, experimental conditions, and neuronal cell-type annotations. This dataset may be reused for studies of neuronal vulnerability, DNA damage responses, and neuroinflammation-related transcriptional changes.
DOI: 10.15468/dl.mnqb84
title: Occurrence Download

description: A dataset containing 651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Morocco",
 "OccurrenceStatus is Present",
 "TaxonKey is Atriplex"
 ]
}

 The dataset includes 651 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003927-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00012544.0
title: Available datapackage for study 'AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5061/dryad.wwpzgmss6
title: Data from: Nest predation and climate change as drivers of alternative reproductive tactics in a migratory species

description: Alternative reproductive tactics enable individuals to choose a
 reproductive tactic relative to their status and prevailing environmental
 conditions in a way that increases their fitness. Conspecific brood
 parasitism, in which females lay eggs in nests of conspecifics, is one
 example of such behaviour. It has been proposed that when prospects for
 successful nesting are low and/or costs of reproduction are high, females
 employ tactics of low cost such as non-breeding and parasitic laying. When
 environmental conditions are ideal and prospects for success high, females
 can increase their reproductive effort above typical nesting by laying
 parasitic eggs prior to initiating their own nest.  
description: Here, I used this flexible life-history strategy concept and
 long-term (1994-2022) population level data of the common goldeneye
 (<em>Bucephala clangula</em>) to study how the rate of
 parasitic laying varies in relation to variation in nest predation risk
 and in the timing and length of the breeding season, the latter being
 measured by the timing of ice breakup.
description: # Data from: Nest predation and climate change as drivers of alternative
 reproductive tactics in a migratory species
 [https://doi.org/10.5061/dryad.wwpzgmss6](https://doi.org/10.5061/dryad.wwpzgmss6) The data include annual (1995-2022) values for the following analyses and variables. In the first sheet (MODEL_VARIABLES) are given data for variables used to analyse among-year variation in the rate of parasitic laying: rate of parasitic laying, ice-out date (IOD), nest predation rate in year t-1, nest predation rate in year t, number of indicated breeding pairs, winter severity index (WSI). In the second sheet (OTHER_VARIABLES) are given data for variables used in other analyses: proportion of predated eggs for non-parasitized nests (Prop_pred_non-par), proportion of predated eggs for parasitized nests (Prop_pred_par), proportion of eggs failed in other way for non-parasitized nests (Prop_fail_non-par), proportion of eggs failed in other way for parasitized nests (Prop_fail_par), overall proportion of failed eggs for non-parasitized nests (Prop_totfail_non-par), overall proportion of failed eggs for parasitized nests (Prop_totfail_par), number of offspring produced per nest for non-parasitized nests (Offspring_non-par), number of offspring produced per nest for parasitized nests (Offspring_par), proportion eggs that produced an offspring that left the nest (Nest_success).