cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.76abfg
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Pheidole bilimeki Mayr, 1870"
 ]
}

 The dataset includes 87 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0115811-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ptj9vd
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apocopis wrightii Munro"
 ]
}

 The dataset includes 103 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040186-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.93z7nf
title: Occurrence Download

description: A dataset listing the 15083 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is USA",
 "OccurrenceStatus is Present",
 "TaxonKey is Agaricomycetes"
 ]
}

 The dataset's 15083 records were derived from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0128494-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tycbsn
title: Occurrence Download

description: A dataset containing 18491 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phalaris canariensis L."
 ]
}

 The dataset includes 18491 records from 616 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072656-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14333134
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.zsrqsg
title: Occurrence Download

description: A dataset listing the 561 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Xeno-canto - Orthoptera sounds from around the world, International Barcode of Life project (iBOL), ZFMK Orthoptera collection, Observation.org, Nature data from around the World, izeltlabuak.hu observations validated to species level, Arthropod observations derived from HabiProt Alciphron database in Serbia, iNaturalist Research-grade Observations, Orthoptera and Mantodea in the Continental biogeographical region and adjacent areas of European Russia, Faunistic and floristic records in the Kharkiv and some other regions in 2022-2024 period and some earlier years, NABU|naturgucker)",
 "Geometry POLYGON((135.30203 47.78819,126.93762 51.497,93.47999 53.29256,72.25918 54.56919,37.0977 51.01229,27.80391 50.1269,18.66502 47.47506,19.90419 44.57207,29.04308 43.45817,49.17962 42.89338,59.09299 47.57964,80.00401 45.66502,97.6622 45.33934,131.89431 44.79234,135.30203 47.78819))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Orthoptera"
 ]
}

 The dataset's 561 records were derived from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068572-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pk245z
title: Occurrence Download

description: A dataset containing 489 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tamarix hispida"
 ]
}

 The dataset includes 489 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068578-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.687mua
title: Occurrence Download

description: A dataset containing 528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is Populus euphratica"
 ]
}

 The dataset includes 528 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068579-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18082927
title: Distinct Gut Microbiota Profiles Across the Alzheimer's Disease Continuum

description: This repository contains de-identified processed metagenomic data supporting the manuscript"Distinct Gut Microbiota Profiles Across the Alzheimer’s Disease Continuum".

Files include combined taxonomic abundance tables (absolute and relative) across multiple taxonomic levels derived from Kraken2 and Bracken outputs, and used for all downstream statistical analyses reported in the manuscript.
DOI: 10.5281/zenodo.18082928
title: Distinct Gut Microbiota Profiles Across the Alzheimer's Disease Continuum

description: This repository contains de-identified processed metagenomic data supporting the manuscript"Distinct Gut Microbiota Profiles Across the Alzheimer’s Disease Continuum".

Files include combined taxonomic abundance tables (absolute and relative) across multiple taxonomic levels derived from Kraken2 and Bracken outputs, and used for all downstream statistical analyses reported in the manuscript.
DOI: 10.25574/31397
title: Chandra X-ray Observatory ObsId 31397

description: AO-27 Big Dither Observation of Mkn421
DOI: 10.15468/dl.y5mvry
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_1_GID","value":"FIN.2_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/0068576-251120083545085/datasets/export for details.
DOI: 10.15468/dl.2ebzju
title: Occurrence Download

description: A dataset containing 423486 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of Minnesota Insect Collection"
 ]
}

 The dataset includes 423486 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068577-251120083545085/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.cc2msgd9
title: CCDC 2436581: Experimental Crystal Structure Determination

description: Related Article: Ming-Bu Luo, Er-Xia Chen, Juan Wang, Jin-Xia Yang, Jian Zhang, Qipu Lin|2025|Angew.Chem.,Int.Ed.|64|e23993|doi:10.1002/anie.202523993
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: 10.60895/redata/gxgohs
title: Breath-by-breath cardiometabolic data during lateral walking

description: This dataset contains structured records from individual cardiometabolic test sessions, extracted from original Excel files. Each entry corresponds to one subject and includes both static metadata and dynamic time-series measurements.

Structure Overview Each JSON block includes the following components: subject_id: Unique identifier for the participant, extracted from the filename or cell A1 of the original Excel sheet.

metadata: Key descriptive fields about the subject and test context. These include: age: Age of the subject at the time of testing height_cm: Height in centimeters weight_kg: Weight in kilograms sex: Biological sex test_type: Type of test performed (e.g., maximal, submaximal) test_date: Date of the test session subject_type: Classification of the subject (e.g., healthy, clinical) birth_date: Date of birth test_reason: Stated reason or objective for the test

data_table: Time-series measurements recorded during the test. This includes: headers: List of variable names (e.g., VO₂, HR, GPS altitude, cadence) units: Corresponding units of measurement for each variable records: List of data rows, each representing a time point or step in the protocol. All columns are preserved to maintain raw data integrity.
description: Python, 3.11.5
DOI: 10.5281/zenodo.18085876
title: cnemidophorus_lemniscatus_spades_01.fasta.gz
DOI: 10.5281/zenodo.18085877
title: cnemidophorus_lemniscatus_spades_01.fasta.gz
DOI: 10.15468/dl.cj2x5c
title: Occurrence Download

description: A dataset containing 1426 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Korean Insect Database"
 ]
}

 The dataset includes 1426 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068582-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14333239
title: Lumbar Spinal MRI Dataset
DOI: 10.15468/dl.4hm7jf
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tamarix taklamakanensis"
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068583-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18086996
title: Low energy state data in the Z_4^3 symmetry protected topological paramagnet
DOI: 10.5281/zenodo.18086997
title: Low energy state data in the Z_4^3 symmetry protected topological paramagnet
DOI: 10.15468/dl.9j4fu3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_1_GID","value":"FIN.1_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/0068581-251120083545085/datasets/export for details.
DOI: 10.6084/m9.figshare.30964396
title: Computational fluid dynamics analysis of hemodynamic characteristics in aortic dissection induced by intramural hematoma

description: In this study, two case models of intramural hematoma (IMH) progressing to aortic dissection were analyzed using computational fluid dynamics, with a focus on the hemodynamic characteristics before and after the dissection. In the initial IMH model, disturbed flows were observed during the early and late phases of contraction within the aortic arch and regions of significant vascular curvature. Furthermore, the IMH models also revealed extensive areas of low time-averaged wall shear stress (TAWSS), high oscillatory shear index (OSI), and high endothelial cell activation potential (ECAP) values, particularly on the lesser-curvature side of the aortic arch. Regions with low TAWSS, high OSI, and high ECAP in IMH cases can be identified as potential high-risk areas for disease progression to aortic dissection.
DOI: 10.6084/m9.figshare.30964396.v1
title: Computational fluid dynamics analysis of hemodynamic characteristics in aortic dissection induced by intramural hematoma

description: In this study, two case models of intramural hematoma (IMH) progressing to aortic dissection were analyzed using computational fluid dynamics, with a focus on the hemodynamic characteristics before and after the dissection. In the initial IMH model, disturbed flows were observed during the early and late phases of contraction within the aortic arch and regions of significant vascular curvature. Furthermore, the IMH models also revealed extensive areas of low time-averaged wall shear stress (TAWSS), high oscillatory shear index (OSI), and high endothelial cell activation potential (ECAP) values, particularly on the lesser-curvature side of the aortic arch. Regions with low TAWSS, high OSI, and high ECAP in IMH cases can be identified as potential high-risk areas for disease progression to aortic dissection.
DOI: 10.6084/m9.figshare.30963895
title: <b>A vertically integrated microbial continuum sustains biogeochemical cycling in the deepest ocean</b>

description: 328 rMAGs from Yap trench waters.
DOI: 10.6084/m9.figshare.30963895.v1
title: <b>A vertically integrated microbial continuum sustains biogeochemical cycling in the deepest ocean</b>

description: 328 rMAGs from Yap trench waters.
DOI: 10.17909/20tm-sg94
title: Data from TRGB-SBF Project Paper IV

description: JWST data used in TRGB-SBF Project Paper IV (Chazov et al.).
DOI: 10.60686/t-fsd3987
title: FSD3987 Hate and Public Sphere: Municipal Councillor Survey 2019-2020
DOI: 10.60686/t-fsd3988
title: FSD3988 Hate and Public Sphere: Media Professionals Survey 2020
DOI: 10.15468/dl.vrtu2n
title: Occurrence Download

description: A dataset containing 134215 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.48297 6.37096,-75.53057 6.32602,-75.53692 6.28896,-75.54882 6.23454,-75.52105 6.17934,-75.45124 6.14306,-75.38936 6.14858,-75.3378 6.147,-75.30924 6.17461,-75.28544 6.24795,-75.30289 6.35598,-75.34811 6.37726,-75.48297 6.37096))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 134215 records from 208 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068580-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rz75ka
title: Occurrence Download

description: A dataset containing 17345 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Gyps bengalensis"
 ]
}

 The dataset includes 17345 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068585-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14333326
title: BOS Kemdikbud
DOI: 10.6084/m9.figshare.29153054
title: Reinforcement Learning-Based Hybrid Force/Position Control of Redundant Manipulators under Time Delays

description: Hybrid force/position control of redundant robotic manipulators under time delays, disturbances, and model uncertainties remains a significant challenge. Conventional methods rely on separating the force and position control spaces and on precise system identification, which increases complexity and reduces robustness. This paper introduces a semi-model-free framework, the Force/Position Reinforcement Learning Super-Twisting Algorithm (F/P-RL-STA), which avoids explicit space separation and reduces dependency on accurate models. By integrating a Super-Twisting Sliding Mode Controller (STSMC) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning, the proposed method enables simultaneous force and position regulation. The reinforcement learning agent adaptively tunes control parameters and null-space torques, while ensuring smooth transitions between free motion and contact interaction. This adaptive mechanism reduces control effort, suppresses chattering, and enhances robustness against disturbances and time delays. Closed-loop stability is guaranteed through Lyapunov analysis. Sensitivity analysis is conducted to guide the tuning of learning parameters and reward design. Simulation results confirm that the proposed approach significantly outperforms conventional methods in terms of accuracy, energy efficiency, smoothness of control signals, and chattering elimination, even in the presence of disturbances and time delays.An agent reinforcement learning architecture is introduced to switch smoothly between position control during free motion and hybrid force/position control during contact tasks. The kinematic redundancy of the OpenManipulator-X robot is exploited using a learned null-space torque to minimize control effort and suppress chattering. A comprehensive sensitivity analysis is performed to guide optimal tuning of learning parameters and the reward function, enhancing training stability and controller robustness. Simulation results confirm that F/P-RL-STA significantly outperforms both SMC-HFPC and F/P-STA in terms of tracking precision, control smoothness, disturbance rejection, and energy efficiency, while maintaining closed-loop stability as verified through Lyapunov-based analysis.
DOI: 10.6084/m9.figshare.29153054.v3
title: Reinforcement Learning-Based Hybrid Force/Position Control of Redundant Manipulators under Time Delays

description: Hybrid force/position control of redundant robotic manipulators under time delays, disturbances, and model uncertainties remains a significant challenge. Conventional methods rely on separating the force and position control spaces and on precise system identification, which increases complexity and reduces robustness. This paper introduces a semi-model-free framework, the Force/Position Reinforcement Learning Super-Twisting Algorithm (F/P-RL-STA), which avoids explicit space separation and reduces dependency on accurate models. By integrating a Super-Twisting Sliding Mode Controller (STSMC) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning, the proposed method enables simultaneous force and position regulation. The reinforcement learning agent adaptively tunes control parameters and null-space torques, while ensuring smooth transitions between free motion and contact interaction. This adaptive mechanism reduces control effort, suppresses chattering, and enhances robustness against disturbances and time delays. Closed-loop stability is guaranteed through Lyapunov analysis. Sensitivity analysis is conducted to guide the tuning of learning parameters and reward design. Simulation results confirm that the proposed approach significantly outperforms conventional methods in terms of accuracy, energy efficiency, smoothness of control signals, and chattering elimination, even in the presence of disturbances and time delays.An agent reinforcement learning architecture is introduced to switch smoothly between position control during free motion and hybrid force/position control during contact tasks. The kinematic redundancy of the OpenManipulator-X robot is exploited using a learned null-space torque to minimize control effort and suppress chattering. A comprehensive sensitivity analysis is performed to guide optimal tuning of learning parameters and the reward function, enhancing training stability and controller robustness. Simulation results confirm that F/P-RL-STA significantly outperforms both SMC-HFPC and F/P-STA in terms of tracking precision, control smoothness, disturbance rejection, and energy efficiency, while maintaining closed-loop stability as verified through Lyapunov-based analysis.
DOI: 10.23645/epacomptox.5588566
title: Distributed Structure-Searchable Toxicity (DSSTox) Database

description: EPA’s Distributed Structure-Searchable Toxicity (DSSTox) database contains curated chemical substances mapped to data including chemical identifiers (i.e., chemical synonyms, systematic names, CAS Registry Numbers and others) and, where appropriate, chemical structure representations. The goal for DSSTox is to accurately represent chemical substances, their structures and identifiers, as well as relevant chemical lists which are important to the environmental research and regulatory community. DSSTox provides the chemical and chemistry underpinning for EPA’s computational toxicology and exposure online applications including the CompTox Chemicals Dashboard, EcoTox Knowledgbase, GenRa, and more. DSSTox data is publicly accessible through these online software applications, a public API and these downloadable excel/csv file. There are several excel files which can be downloaded from this page. Collectively, these excel files include a listing of the over 1.2 million chemicals in the EPA CompTox Chemicals Dashboard. The files list each chemical and provide the mapping of the chemical to CAS RN, INCHIKEY, IUPAC-Name, SMILES, Molecular Formulae, Average Mass, Monoisotopic Mass, QSAR-Ready SMILES, MS-Ready SMILES and Identifiers.<br>Dataset originally posted on 10/21/2021<br>Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.23645/epacomptox.5588566.v8
title: Distributed Structure-Searchable Toxicity (DSSTox) Database

description: EPA’s Distributed Structure-Searchable Toxicity (DSSTox) database contains curated chemical substances mapped to data including chemical identifiers (i.e., chemical synonyms, systematic names, CAS Registry Numbers and others) and, where appropriate, chemical structure representations. The goal for DSSTox is to accurately represent chemical substances, their structures and identifiers, as well as relevant chemical lists which are important to the environmental research and regulatory community. DSSTox provides the chemical and chemistry underpinning for EPA’s computational toxicology and exposure online applications including the CompTox Chemicals Dashboard, EcoTox Knowledgbase, GenRa, and more. DSSTox data is publicly accessible through these online software applications, a public API and these downloadable excel/csv file. There are several excel files which can be downloaded from this page. Collectively, these excel files include a listing of the over 1.2 million chemicals in the EPA CompTox Chemicals Dashboard. The files list each chemical and provide the mapping of the chemical to CAS RN, INCHIKEY, IUPAC-Name, SMILES, Molecular Formulae, Average Mass, Monoisotopic Mass, QSAR-Ready SMILES, MS-Ready SMILES and Identifiers.<br>Dataset originally posted on 10/21/2021<br>Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25