cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17987149
title: Particulate simulation unveils the functional morphology and ecology of the earliest metazoan reef builders

description: This is the velocity distribution data of the different particulate simulations with different models, velocities or permeabilities.
DOI: 10.15468/dl.jh9yrd
title: Occurrence Download

description: A dataset containing 563 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tracheophyta)",
 "Geometry POLYGON ((12.91808 47.55457, 12.95308 47.55457, 12.95308 47.58957, 12.91808 47.58957, 12.91808 47.55457))"
 ]
}

 The dataset includes 563 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060359-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fbaqwm
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.26_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060358-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5bk6xr
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((69.22909 36.86476,105.53152 36.86476,105.53152 56.97769,69.22909 56.97769,69.22909 36.86476))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Vormela peregusna"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060360-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a8dex8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.27_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060361-251120083545085/datasets/export for details.
DOI: 10.15468/dl.rpyyxt
title: Occurrence Download

description: A dataset containing 166136 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geranium pusillum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 166136 records from 1069 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060336-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24435/materialscloud:6w-xv
title: Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

description: Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO–LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes.
DOI: 10.24435/materialscloud:dn-pf
title: Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

description: Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO–LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes.
DOI: 10.24435/materialscloud:7e-17
title: A universal machine learning model for the electronic density of states

description: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.
DOI: 10.24435/materialscloud:gs-z7
title: A universal machine learning model for the electronic density of states

description: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.
DOI: 10.15468/dl.2rn9du
title: Occurrence Download

description: A dataset containing 29693 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geum macrophyllum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 29693 records from 248 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060362-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17985510
title: High-quality harmonized and standardized PFAS toxicogenomics data collection
title: AOP-based Analysis of Curated Transcriptomic Data Reveals a Context-dependent Core Mechanism of PFAS-induced Liver Steatosis

description: This dataset provides a curated, harmonized, and standardized collection of toxicogenomic data for per- and polyfluoroalkyl substances (PFAS), developed to support mechanistic, context-aware hazard characterization within a precision toxicology framework.

The collection integrates publicly available transcriptomic datasets generated following PFAS exposure across multiple biological systems, species, exposure durations, and concentrations. All datasets were subjected to rigorous quality control, standardized preprocessing, and harmonization to ensure interoperability and comparability across studies. To address critical data gaps, particularly in immune-related contexts, the collection also includes newly generated transcriptomic profiles from PMA-differentiated THP-1 macrophages exposed to seven PFAS (legacy and emerging alternatives) at multiple subtoxic, equipotent concentrations and time points (24 h, 48 h, and 72 h).

The dataset is specifically structured to enable integration with the Adverse Outcome Pathway (AOP) framework and supports the reconstruction of context-dependent mechanistic pathways linking molecular initiating events to downstream key events and adverse outcomes. 

The repository includes:





Entry-level metadata


Normalized expression matrices



Raw count matrix


Filtered and unfiltered differentially expressed genes



Scripts and workflows used for data curation, standardization, preprocessing, and subsequent analysis


A detailed table of entry-level characteristics.


This resource addresses current limitations in chemical risk assessment by enabling multi-chemical, multi-endpoint, and biologically contextualized analyses. The dataset is fully interoperable and extensible, allowing future integration of additional toxicogenomic datasets processed using the same quality standards and workflows, and can be readily adapted to other chemical classes.
DOI: 10.5281/zenodo.17985509
title: High-quality harmonized and standardized PFAS toxicogenomics data collection
title: AOP-based Analysis of Curated Transcriptomic Data Reveals a Context-dependent Core Mechanism of PFAS-induced Liver Steatosis

description: This dataset provides a curated, harmonized, and standardized collection of toxicogenomic data for per- and polyfluoroalkyl substances (PFAS), developed to support mechanistic, context-aware hazard characterization within a precision toxicology framework.

The collection integrates publicly available transcriptomic datasets generated following PFAS exposure across multiple biological systems, species, exposure durations, and concentrations. All datasets were subjected to rigorous quality control, standardized preprocessing, and harmonization to ensure interoperability and comparability across studies. To address critical data gaps, particularly in immune-related contexts, the collection also includes newly generated transcriptomic profiles from PMA-differentiated THP-1 macrophages exposed to seven PFAS (legacy and emerging alternatives) at multiple subtoxic, equipotent concentrations and time points (24 h, 48 h, and 72 h).

The dataset is specifically structured to enable integration with the Adverse Outcome Pathway (AOP) framework and supports the reconstruction of context-dependent mechanistic pathways linking molecular initiating events to downstream key events and adverse outcomes. 

The repository includes:





Entry-level metadata


Normalized expression matrices



Raw count matrix


Filtered and unfiltered differentially expressed genes



Scripts and workflows used for data curation, standardization, preprocessing, and subsequent analysis


A detailed table of entry-level characteristics.


This resource addresses current limitations in chemical risk assessment by enabling multi-chemical, multi-endpoint, and biologically contextualized analyses. The dataset is fully interoperable and extensible, allowing future integration of additional toxicogenomic datasets processed using the same quality standards and workflows, and can be readily adapted to other chemical classes.
DOI: 10.34740/kaggle/dsv/14223656
title: textocr
DOI: 10.15468/dl.y7jgq6
title: Occurrence Download

description: A dataset containing 17290 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Geum triflorum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 17290 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060363-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/wbvlap
title: Experimental dataset on the characterization of a commercial liquid smoke flavoring, its microencapsulation by spray drying with whey protein isolate and maltodextrin, and on the characterization of the resulting spray-dried smoke powders

description: This dataset contains the raw data generated during the master’s research on the microencapsulation of liquid smoke by spray drying. The data are divided into three main parts: (1) characterization of the liquid smoke, encompassing volatile compounds analysis — with comprehensive GC-MS chromatographic fingerprints for global volatile profiling and targeted GC-FID chromatograms for estimating the area values of selected marker compounds — together with its physicochemical characterization; (2) microencapsulation process, with operating parameters and feed formulations, and mass balance of the process; and (3) characterization of the resulting microparticles, including moisture content, water activity, particle size distribution, morphology, and also GC-MS and GC-FID chromatograms. The dataset generally provides raw chromatograms (in PDF and instrument-native formats) and experimental spreadsheets, exported directly from the instruments/software without any post-processing. In cases where a file contains processed data, this is explicitly stated in its individual description.
DOI: 10.15468/dl.asmaqf
title: Occurrence Download

description: A dataset containing 28519 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tracheophyta)",
 "Geometry POLYGON ((12.92553 47.54475, 12.96053 47.54475, 12.96053 47.57975, 12.92553 47.57975, 12.92553 47.54475))"
 ]
}

 The dataset includes 28519 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060364-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23637/rms-rmaatempanomaly-1
title: Dataset: Annual Mean Air Temperature Anomaly at Rothamsted 1878-2019

description: Rothamsted has one of the longest running continuous sets of daily meteorological recordings in the world. Temperature has been measured since 1878. The figure above shows the temperature anomaly in annual mean air temperature (black circles), for years 1878 to 2019, relative to the 1878 to 1920 long-term average, which represents the pre-industrial era. Variation in annual temperature is smoothed using an 11-year (132 month) moving average (red line, which removes solar variability and shorter-term variability such as the Southern Oscillation (Hansen et. al., 2010)). The figure shows that air temperatures have risen sharply since 1987 (Scott, 2015) with annual temperatures, over the last decade, estimated to be between 1.3°C and 1.6°C higher than the pre-industrial period (Hansen. et. al., 2016). Much of the rise is due to increases in temperature during the autumn and winter months, and at night-time.
description: The mean daily temperature is measured over a 24 hour period 0900 to 0900 GMT and calculated from the daily maximum (Tmax) and minimum temperatures (Tmin); the mean daily temperature = (Tmax + Tmin)/2. For each month the mean air temperature is determined from daily data, also the annual mean air temperature for each year (1878 to 2019). The long-term monthly average and annual average for 1878 to 1920 is also determined. The annual mean temperature anomaly and 132 month (11 year) moving average is determined from these annual and monthly air temperature data using Excel functions.
description: First recorded in 1878, TMAX was originally recorded using a mercury-in-glass with indicator bar minimum thermometer and TMIN using a spirit-in-glass with indicator bar minimum thermometer, both housed in a naturally aspiring temperature screen. Since 2004, temperatures have been recorded automatically by thermistors (electronic temperature probes, Campbell Scientific, Type 107).
description: For further information about the measurement of temperature at Rothamsted see 'Measurements'. For further information about extreme temperature records at Rothamsted see 'Extremes'.
DOI: 10.23637/keyrefoaresmatemp
title: Dataset: Annual Mean Air Temperature at Rothamsted 1878-2013

description: Rothamsted has one of the longest running continuous sets of meteorological recordings in the world. The figure shows the annual mean air temperature at Rothamsted every year from 1878-2013. Also shown is the mean over each five-year period, 1878-1882, 1883-1887, etc. The red line shows the mean temperature, 1878-1987. Mean annual air temperature has fluctuated considerably between 1878-2013. However, when the variation in annual temperature is smoothed using five-year means, it is apparent that air temperatures have risen sharply since 1987 (Scott, 2015). The mean air temperature at Rothamsted is now approximately 10.04°C which is 1.1°C higher than the 1878-1987 average. The 10 warmest years on record occurred in the last 17 years. Much of the rise is due to increases in the autumn and winter months, and in night time temperatures.
description: Summary data used for this chart are derived from annual daily data measured at Rothamsted Meteorological Station. Daily temperature is measured over the 24 hour period 0900 to 0900 GMT.
description: Since first recorded in 1878, maximum temperature (TMAX) was measured using a mercury column thermometer and mimimum temperature (TMIN) using a spirit-in-glass with indicator bar minimum thermometer. 
 Since 2004, temperatures have been recorded automatically by thermistors (electronic temperature probes, Campbell Scientific, Type 107).
DOI: 10.23637/rmsmmrain10850917
title: Dataset: Mean monthly rainfall at Rothamsted October 1985-September 2017

description: This dataset represents the mean monthly rainfall recorded at Rothamsted from October 1985 - September 2017 and is derived from continuous daily records measured at the site.
description: Daily rainfall is measured as the total (mm) over the 24-hour period 0900 to 0900 GMT. It includes all precipitation - snow, rain, mist and fog. Rainfall was first recorded at Rothamsted in March 1853, using a copper funnel rain gauge (5 inch / 12.7 cm diameter) and measured using a graduated cylinder. Since 2004 it has been measured using an electronic tipping bucket rain gauge (10 inch / 25.4cm diameter), ARG100, calibrated to tip at 0.2mm (which has since become the minimum amount of rain that can be recorded). The rain gauge is placed within a 30cm deep 1.5m radius turf wall, retained by brick, to reduce wind eddies that may potentially blow rain out of the gauges. Data were collected daily manually until 2004 and since then by Automatic Weather Station using a standard protocol. There are differences in the capture rate between the two gauges, see Rainfall for further information.
description: Location: Rothamsted Meteorological Station, Harpenden, Hertfordshire, England 51.82 N 0.37 W 128 m asl.
description: The monthly summary data contained in this spreadsheet are derived from daily data measured at Rothamsted Meteorological Station, Harpenden. Total monthly data is determined from daily data using Genstat 19th Edition. Verification includes checks for instrument errors, for missing data and outliers. The original raw daily data is available, after registering, from the e-RA database. Please contact the e-RA Curators for an access password and further details.