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

description: A dataset containing 4238 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mandragora officinarum"
 ]
}

 The dataset includes 4238 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017759-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vvz8x8
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus cyanostictus Axtell & Axtell, 1971"
 ]
}

 The dataset includes 179 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001819-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.493rfy
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus druckercolini Perez-Ramos & Saldana De La Riva, 2008"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001820-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6dfnz
title: Occurrence Download

description: A dataset containing 946 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus dugesii Bocourt, 1874"
 ]
}

 The dataset includes 946 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001821-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2kw41n
title: CCDC 2379561: Experimental Crystal Structure Determination

description: Related Article: Mao-Hao Li, Zheng Wang, Yanni Liang, Simeng Lu, Bao-Long Hou|2025|J.Mol.Struct.|1342|142651|doi:10.1016/j.molstruc.2025.142651
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10907411
title: ChromBERT: Uncovering Chromatin State Motifs in the Human Genome using a BERT-based Approach

description: Pretrain data and results for: 



Promoter regions for all genes in 127 different cell lines in ROADMAP 

CRM (cis-regulatory module) regions longer than 2k bps in 127 different cell lines in ROADMAP

Whole genome regions without continuous low signal state ("O") in 4-mer



Fine-tuning data and result for:



[Classification] Promoter regions of high-expressed genes (from RPKM>10 to RPKM>50) compared to not expressed genes (RPKM=0) or low-expressed genes (RPKM>0) with the directory names:



not_n_rpkm0 : RPKM=0 vs. RPKM>0

not_n_rpkm10 : RPKM=0 vs. RPKM>10

not_n_rpkm20 : RPKM=0 vs. RPKM>20 

not_n_rpkm30 : RPKM=0 vs. RPKM>30 

not_n_rpkm50 : RPKM=0 vs. RPKM>50 

rpkm0_n_rpkm10 : RPKM>0 vs. RPKM>10 

rpkm0_n_rpkm20 : RPKM>0 vs. RPKM>20 

rpkm0_n_rpkm30 : RPKM>0 vs. RPKM>30 

rpkm0_n_rpkm50 : RPKM>0 vs. RPKM>50 

rpkm10_n_rpkm20 : RPKM>10 vs. RPKM>20 

rpkm10_n_rpkm30 : RPKM>10 vs. RPKM>30 

rpkm10_n_rpkm50 : RPKM>10 vs. RPKM>50 

rpkm20_n_rpkm30 : RPKM>20 vs. RPKM>30 

rpkm20_n_rpkm50 : RPKM>20 vs. RPKM>50 

rpkm30_n_rpkm50 : RPKM>30 vs. RPKM>50 



[Regression] Quantitative gene expression prediction for promoter regions

CRM regions longer than 2k bps compared to non-CRM regions
DOI: 10.5281/zenodo.10907412
title: ChromBERT: Uncovering Chromatin State Motifs in the Human Genome using a BERT-based Approach

description: Pretrain data and results for: 



Promoter regions for all genes in 127 different cell lines in ROADMAP 

CRM (cis-regulatory module) regions longer than 2k bps in 127 different cell lines in ROADMAP

Whole genome regions without continuous low signal state ("O") in 4-mer



Fine-tuning data and result for:



Promoter regions of high-expressed genes (from RPKM>10 to RPKM>50) compared to not expressed genes (RPKM=0) or low-expressed genes (RPKM>0) with the directory names:



not_n_rpkm0 : RPKM=0 vs. RPKM>0

not_n_rpkm10 : RPKM=0 vs. RPKM>10

not_n_rpkm20 : RPKM=0 vs. RPKM>20 

not_n_rpkm30 : RPKM=0 vs. RPKM>30 

not_n_rpkm50 : RPKM=0 vs. RPKM>50 

rpkm0_n_rpkm10 : RPKM>0 vs. RPKM>10 

rpkm0_n_rpkm20 : RPKM>0 vs. RPKM>20 

rpkm0_n_rpkm30 : RPKM>0 vs. RPKM>30 

rpkm0_n_rpkm50 : RPKM>0 vs. RPKM>50 

rpkm10_n_rpkm20 : RPKM>10 vs. RPKM>20 

rpkm10_n_rpkm30 : RPKM>10 vs. RPKM>30 

rpkm10_n_rpkm50 : RPKM>10 vs. RPKM>50 

rpkm20_n_rpkm30 : RPKM>20 vs. RPKM>30 

rpkm20_n_rpkm50 : RPKM>20 vs. RPKM>50 

rpkm30_n_rpkm50 : RPKM>30 vs. RPKM>50 



CRM regions longer than 2k bps compared to non-CRM regions
DOI: 10.5281/zenodo.15518584
title: ChromBERT: Uncovering Chromatin State Motifs in the Human Genome using a BERT-based Approach

description: Pretrain data and results for: 



Promoter regions for all genes in 127 different cell lines in ROADMAP 

CRM (cis-regulatory module) regions longer than 2k bps in 127 different cell lines in ROADMAP

Whole genome regions without continuous low signal state ("O") in 4-mer



Fine-tuning data and result for:



[Classification] Promoter regions of high-expressed genes (from RPKM>10 to RPKM>50) compared to not expressed genes (RPKM=0) or low-expressed genes (RPKM>0) with the directory names:



not_n_rpkm0 : RPKM=0 vs. RPKM>0

not_n_rpkm10 : RPKM=0 vs. RPKM>10

not_n_rpkm20 : RPKM=0 vs. RPKM>20 

not_n_rpkm30 : RPKM=0 vs. RPKM>30 

not_n_rpkm50 : RPKM=0 vs. RPKM>50 

rpkm0_n_rpkm10 : RPKM>0 vs. RPKM>10 

rpkm0_n_rpkm20 : RPKM>0 vs. RPKM>20 

rpkm0_n_rpkm30 : RPKM>0 vs. RPKM>30 

rpkm0_n_rpkm50 : RPKM>0 vs. RPKM>50 

rpkm10_n_rpkm20 : RPKM>10 vs. RPKM>20 

rpkm10_n_rpkm30 : RPKM>10 vs. RPKM>30 

rpkm10_n_rpkm50 : RPKM>10 vs. RPKM>50 

rpkm20_n_rpkm30 : RPKM>20 vs. RPKM>30 

rpkm20_n_rpkm50 : RPKM>20 vs. RPKM>50 

rpkm30_n_rpkm50 : RPKM>30 vs. RPKM>50 



[Regression] Quantitative gene expression prediction for promoter regions

CRM regions longer than 2k bps compared to non-CRM regions
DOI: 10.48573/5jx6-hk67
title: DDS21, Cross Sectional Survey Wave 2: Popular Vote on 03.03.2024
DOI: 10.48573/t1zk-x995
title: DDS21, Cross Sectional Survey Wave 2: Popular Vote on 03.03.2024
DOI: 10.6084/m9.figshare.29149838
title: Gut Microbiome Data from a Clinical Trial on Mare's Milk Intake in Psoriasis Patients

description: This dataset supports the study titled <i>“Modulating Psoriasis Microbiome: A Clinical Trial on Mare's Milk Impact.”</i> Psoriasis is a chronic inflammatory skin condition associated with gut microbiome dysbiosis. This non-randomized controlled trial investigated the effects of daily ingestion of freeze-dried mare’s milk over 12 weeks in adult psoriasis patients.The study included 50 adult participants (20 psoriasis cases and 30 controls), with faecal samples collected at baseline (Week 1), Week 6, and Week 12. Microbiome profiling was conducted via 16S rRNA gene sequencing. This dataset includes raw and processed microbiome sequencing data, metadata (age, group, timepoint, PASI scores), and statistical outputs relevant to alpha and beta diversity, taxonomic changes, and effect size estimations.The most notable findings include a psoriasis-associated reduction in the phylum Actinobacteriota, which was significantly restored following mare’s milk intake, alongside clinical improvements in PASI scores. This dataset may be reused for comparative microbiome studies, validation of psoriasis-associated microbial signatures, or investigations of prebiotic interventions in skin-gut axis disorders.
DOI: 10.6084/m9.figshare.29149736
title: Spatial and Temporal Evolution of Tropical Rainforest Biomass in Hainan Based on Multi-modal Temporal Remote Sensing Data Fusion

description: This study integrated ground-based plot surveys and multi-source remote sensing data to develop a canopy height and aboveground biomass estimation model for Hainan Tropical Rainforest National Park from 2003 to 2023. To support scientific transparency and model reproducibility, a portion of the research data has been organized and made publicly available. The dataset includes the following three categories:<b>1</b><b>、</b><b>Survey data of tropical rainforest plots</b><b>：</b><b>Biomass data of 140 sample plots:</b> Includes 140 sample plots (10 m × 10 m), with information on plot ID, geographic coordinates, forest type, and biomass of tree and understory layers.<b>Biomass data of 64 historical plots:</b> Contains biomass data from 64 historical plots (25.8 m × 25.8 m), including plot ID, location, and aboveground biomass.<b>Data of individual trees from 140 sample plots: </b>Covers detailed measurements of 4,732 individual trees, including species name, diameter at breast height (DBH), tree height, crown volume, and estimated biomass per tree.<b>2</b><b>、</b><b>Forest type distribution data</b><b>：</b>Based on the natural forest classification dataset, GLC_FCS30D land cover data, and ALOS DEM elevation data, this shapefile represents forest type distribution in Hainan Tropical Rainforest National Park from 2003 to 2023 at a 30-meter spatial resolution (WGS 1984 coordinate system).<b>3</b><b>、</b><b>Remote sensing estimation results of biomass and canopy height of tropical rainforests in Hainan from 2003 to 2023</b><b>：</b>Includes annual maps of forest aboveground biomass density and canopy height distribution in Hainan Tropical Rainforest National Park from 2003 to 2023. All data are provided in GeoTIFF format with a spatial resolution of 30 meters, suitable for GIS-based analysis and visualization.
DOI: 10.15468/dl.2cydm5
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-88.64723 4.24421,-52.35277 4.24421,-52.35277 18.75579,-88.64723 18.75579,-88.64723 4.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Diplobatis guamachensis Martín Salazar, 1957"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001824-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15519212
title: MSclassifR: datasets for experiments

description: This Zenodo entry provides open-access datasets in R .RData format that were used to reproduce the experiments described in the article "MSclassifR: an R Package for Supervised Classification of Mass Spectra with Machine Learning Methods".

data_COVID: 362 nasal swab mass spectra obtained from 312 patients categorized as either SARS-CoV-2 infected (211 mass spectra) or SARS-CoV-2 uninfected (151 mass spectra) from the Nachtigall et al. study (Nachtigall et al., 2020). The raw data are available at https://www.ebi.ac.uk/pride/archive/projects/PXD021388.

dataset_ShigaTOX: 882 mass spectra from 294 strains of Escherichia coli, classified into two categories: Shiga toxin producer (190 strains, 570 spectra) or non-producer (104 strains, 312 spectra) from the Christner et al. study (Christner et al., 2014). The raw data are available at https://zenodo.org/record/4996418.
DOI: 10.5281/zenodo.15519213
title: MSclassifR: datasets for experiments

description: This Zenodo entry provides open-access datasets in R .RData format that were used to reproduce the experiments described in the article "MSclassifR: an R Package for Supervised Classification of Mass Spectra with Machine Learning Methods".

data_COVID: 362 nasal swab mass spectra obtained from 312 patients categorized as either SARS-CoV-2 infected (211 mass spectra) or SARS-CoV-2 uninfected (151 mass spectra) from the Nachtigall et al. study (Nachtigall et al., 2020). The raw data are available at https://www.ebi.ac.uk/pride/archive/projects/PXD021388.

dataset_ShigaTOX: 882 mass spectra from 294 strains of Escherichia coli, classified into two categories: Shiga toxin producer (190 strains, 570 spectra) or non-producer (104 strains, 312 spectra) from the Christner et al. study (Christner et al., 2014). The raw data are available at https://zenodo.org/record/4996418.
DOI: 10.15468/dl.j9d2mq
title: Occurrence Download

description: A dataset containing 220517 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.continent = 'AFRICA'
 AND occurrence.countrycode = 'TZ'
 AND LOWER(occurrence.iucnredlistcategory) IN (LOWER('EX'), LOWER('EW'), LOWER('CR'), LOWER('EN'), LOWER('VU'), LOWER('NT'), LOWER('LC'), LOWER('DD'), LOWER('NE'))
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 1923 AND occurrence."year" &lt;= 2025)
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 220517 records were derived from 876 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001823-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10605092
title: Quantifying the hardness of bioactivity prediction tasks for transfer learning

description: This dataset contains the following information about FS-Mol dataset:



Embedding of all the molecules in each task with different featurization methods

External chemical distance between train and test tasks caculated with optimal transport dataset distance (OTDD) method

External protein distance between train and test tasks calculated from ESM-2 respresentation of the proteins

Internal chemical hardness (which is a random forest for all the train and test tasks)

Prototypical network performance on the test tasks

Random forest performance on the test tasks


 

Paper Abstract:

Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multi-task learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks, in other words, how difficult (“hard”) a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning, we demonstrate that the proposed task hardness metric is inversely correlated with performance. The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning.
DOI: 10.5281/zenodo.10605093
title: Quantifying the hardness of bioactivity prediction tasks for transfer learning

description: This dataset contains the following information about FS-Mol dataset:



Embedding of all the molecules in each task with different featurization methods

External chemical distance between train and test tasks caculated with optimal transport dataset distance (OTDD) method

External protein distance between train and test tasks calculated from ESM-2 respresentation of the proteins

Internal chemical hardness (which is a random forest for all the train and test tasks)

Prototypical network performance on the test tasks

Random forest performance on the test tasks


 

Paper Abstract:

Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multi-task learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks, in other words, how difficult (“hard”) a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning, we demonstrate that the proposed task hardness metric is inversely correlated with performance. The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning.
DOI: 10.5281/zenodo.13934888
title: Climatological normals and indicators for Brazilian municipalities

description: Monthly climatological normals for the 1961-1990 period and monthly aggregated indicators from 1991 to 2023 for all Brazilian municipalities, computed with indicators from the BR-DWGD project, including maximum and minimum temperature, precipitation, humidity, wind speeed, solar radiation and evapotranspiration.

The methodology to create this dataset is detailed here.
DOI: 10.5281/zenodo.15519719
title: Climatological normals and indicators for Brazilian municipalities

description: Monthly climatological normals for the 1961-1990 period and monthly aggregated indicators from 1991 to 2023 for all Brazilian municipalities, computed with indicators from the BR-DWGD project, including maximum and minimum temperature, precipitation, humidity, wind speeed, solar radiation and evapotranspiration.

The methodology to create this dataset is detailed here.
DOI: 10.15468/dl.tk8s9x
title: Occurrence Download

description: A dataset containing 3134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Charaxes vansoni van Someren, 1957, Charaxes viola brainei van Son, 1966, Acmaeodera serena Fall, 1899, Darna metaleuca (Walker, 1862), Thorybes mexicana nevada Scudder, 1872, Eucarazzia elegans (Ferrari, 1872), Aphis pentstemonicola Gillette & Palmer, 1929, Pyramidobela quinquecristata Braun, 1921, Meris alticola Hulst, 1896, Pseudomasaris occidentalis (Cresson, 1871), Aegyptobia montana Bury & Krantz, 1977, Atoposmia elongata (Michener, 1936), Steinernema feltiae (Filipjev, 1934) Wouts, Mráček, Gerdin & Bedding, 1982, Mestolobes pyropa Meyrick, 1899, Tirumala formosa (Godman, 1880), Euclasta splendidalis Herrich-Schäffer, 1848, Clybeccus declivus (Dlabola, 1986), Sympiesis acrobasidis Miller, 1970, Sympiesis stigmata Girault, 1917, Hesperoctenes eumops Ferris & Usinger, 1939, Tiamastus cavicola (Weyenbergh, 1881), Adoratopsylla intermedia (Wagner, 1901), Hectopsylla pulex (Haller, 1880), Hormopsylla trux Jordan, 1950, Anoplius americanus americanus, Euplectrus koebelei Crawford, 1911, Ceratosolen nexilis Wiebes, 1980, Ramularia bistortae Fuckel, Bostrichonema alpestre Ces., Puccinia septentrionalis Juel, Acanthostigmella genuflexa Höhn., Calycellina phalaridis (Lib. ex P.Karst.) Höhn., Crocicreas tomentosum (Dennis) S.E.Carp., Lichenopeltella palustris (J.P.Ellis) P.M.Kirk & Minter, Massariosphaeria mosana (Mouton) Leuchtm., Aprostocetus verutus Graham, 1961, Macroglenes paludum (Graham, 1969), Trichomalopsis microptera (Lindeman, 1887), Uredinopsis filicina (Niessl) Magnus, Septoria polygoni Sousa da Câmara, Coleosporium phellodendri Kom., 1899, Steringophorus dorsolineatus (Reimer, 1985) Bray, 1995, Habrolepis occidua Annecke & Mynhardt, 1970, Pseudotobothrium dipsacum (Linton, 1897) Dollfus, 1942, Otobothrium insigne Linton, 1905, Philometra charlestonensis Moravec, de Buron, Baker & González-Solís, 2008, Callitetrarhynchus speciosus (Linton, 1897) Carvajal & Rego, 1985, Tympanis ligustri Tul. & C.Tul., Encarsia trjapitzini Yasnosh, 1989, Encarsia marinikia Yasnosh, 1989)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3134 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001825-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.