cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.h73877
title: Occurrence Download

description: A dataset containing 151597362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Year 2024-2025"
 ]
}

 The dataset includes 151597362 records from 499 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001056-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/vm4pjy
title: Understanding the Meanings of Ownership

description: The Measures for Advancing Gender Equality (MAGNET) initiative aims to broaden and deepen the measurement of women’s agency, based on the development of new tools and rigorous testing and comparison of both new and existing methods for measuring agency, and promoting the adoption of these measures at scale. By increasing the availability of innovative meaningful measures of agency for a broad range of contexts, we hope our work will lead to an improved understanding of what women’s agency is, how it manifests and how it can best be measured across contexts given the research question at hand.&lt;/p&gt;
Expanding women’s asset ownership is key for improving gender equality and promoting economic development and well-being. A widespread challenge in data collection is that ownership can have different meanings across contexts, particularly regarding which components of the bundle of rights comprise ownership. Yet, surveys often implicitly assume that all rights are held by the same person. This tool, Understanding the Meanings of Ownership, allows us to elicit responses regarding subjective assessments of what ownership entails by presenting different scenarios in which the main premise is a woman owning a particular asset, but scenarios differ on the rights that women have over the asset. The multiple questions aim to assess how the answers may vary by type of asset and women’s status in the household (living with a partner, living with in-laws, living with her parents). To fully understand individuals’ understanding of ownership, it is useful to have this tool alongside modules that solicit information on individual’s ownership of the asset.
&lt;/p&gt;
This data study includes following files.
&lt;/p&gt;1. A survey document (including implementation guidelines)
&lt;/p&gt;2.Two files, CAPI_Choices and CAPI_Survey, along with the accompanying help files, can be used to construct a CAPI program ready for survey implementation. Alternatively, users can use an Excel workbook "CAPI_.xlsx" that includes worksheets for survey and choices, along with others, for constructing a CAPI program ready for survey implementation.
description: Tools
DOI: 10.60520/ieda/114238
title: Database of mineral chemical compositions within the uppermost lithospheric mantle exposed and sampled in the Wadi Tayin Massif from the Oman ophiolite.

description: The dataset includes in-situ major element chemical analyses realized at the Electron Probe Micro-Analyzer (EPMA) at the Dipartimento di Scienze della Terra “Ardito Desio”, University of Milano, on olivine, orthopyroxene, clinopyroxene, and spinel of spinel harzburgite samples. Samples were collected in the uppermost lithospheric mantle portion exposed along the Wadi Zeeb and Wadi Nassif rivers in the Wadi Taiyn massif (south of the Oman ophiolite). The purpose of the dataset is to document the chemical composition and possible chemical changes of the main mineral phases componing the refractory mantle harzburgites sampled at various depths in the mantle and involved within the spinel stability field by a reactive porous melt flow event with different degrees of melt-rock interaction.
DOI: 10.15468/dl.swytr4
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-87.72716 42.26164,-92.22453 41.36689,-98.01326 36.75697,-121.22236 35.87092,-120.2636 33.63606,-98.74017 32.79797,-86.83356 41.17486,-87.72716 42.26164))",
 "TaxonKey is Arthraxon"
 ]
}

 The dataset includes 68 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001081-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4jc62d
title: Occurrence Download

description: A dataset containing 21344 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bonasa bonasia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 21344 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001080-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17488257
title: Parameters and data files to support the ADAPT immune receptor design pipeline and paper

description: Datafiles to support the ADAPT design pipeline and paper.

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Amir Motmaen, Kevin M. Jude, Nan Wang, Anastasia Minervina, David Feldman, Mauriz A. Lichtenstein, Abishai Ebenezer, Colin Correnti, Paul G. Thomas, K. Christopher Garcia*, David Baker*, Philip Bradley*

(*- corresponding authors)

https://www.biorxiv.org/content/10.1101/2025.11.19.689381v1

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular proteins for surveillance by T cells. The precise recognition of foreign or mutated peptide–MHC (pMHC) complexes by T cell receptors (TCRs) is central to immune defense against pathogens and tumors. Although patient-derived TCRs specific for cancer-associated antigens have been used to engineer tumor-targeting therapies, their reactivity toward self- or near-self antigens may be constrained by negative selection in the thymus. Here, we introduce a structure-based deep learning framework, ADAPT (Antigen-receptor Design Against Peptide-MHC Targets), for the design of TCRs and antibodies that bind to pMHC targets of interest. We evaluate the ADAPT pipeline by designing and characterizing TCRs and antibodies against a diverse panel of pMHCs. Cryogenic electron microscopy structures of two designed antibodies bound to their respective pMHC targets demonstrate atomic-level accuracy at the recognition interface, supporting the robustness of our structure-based approach. Computationally designed TCRs and antibodies targeting pMHC complexes could enable a broad range of therapeutic applications, from cancer immunotherapy to autoimmune disease treatment, and insights gained from TCR–pMHC design should advance predictive understanding of TCR specificity with implications for basic immunology and clinical diagnostics.

Special thanks to Joe Watson and Nate Bennett for help with the fine-tuned RFab parameters (https://github.com/RosettaCommons/RFantibody), to the Alphafold2 developers for making their code and parameter sets freely available (https://github.com/google-deepmind/alphafold), to the creators of the SAbDab database (https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab) for sharing parsed antibody information, and to all the structural biologists whose work (https://www.rcsb.org/) makes structure-based approaches like this possible.

Please contact Phil Bradley with questions: pbradley at fredhutch dot org
DOI: 10.5281/zenodo.17488258
title: Parameters and data files to support the ADAPT immune receptor design pipeline and paper

description: Datafiles to support the ADAPT design pipeline and paper.

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Amir Motmaen, Kevin M. Jude, Nan Wang, Anastasia Minervina, David Feldman, Mauriz A. Lichtenstein, Abishai Ebenezer, Colin Correnti, Paul G. Thomas, K. Christopher Garcia*, David Baker*, Philip Bradley*

(*- corresponding authors)

https://www.biorxiv.org/content/10.1101/2025.11.19.689381v1

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular proteins for surveillance by T cells. The precise recognition of foreign or mutated peptide–MHC (pMHC) complexes by T cell receptors (TCRs) is central to immune defense against pathogens and tumors. Although patient-derived TCRs specific for cancer-associated antigens have been used to engineer tumor-targeting therapies, their reactivity toward self- or near-self antigens may be constrained by negative selection in the thymus. Here, we introduce a structure-based deep learning framework, ADAPT (Antigen-receptor Design Against Peptide-MHC Targets), for the design of TCRs and antibodies that bind to pMHC targets of interest. We evaluate the ADAPT pipeline by designing and characterizing TCRs and antibodies against a diverse panel of pMHCs. Cryogenic electron microscopy structures of two designed antibodies bound to their respective pMHC targets demonstrate atomic-level accuracy at the recognition interface, supporting the robustness of our structure-based approach. Computationally designed TCRs and antibodies targeting pMHC complexes could enable a broad range of therapeutic applications, from cancer immunotherapy to autoimmune disease treatment, and insights gained from TCR–pMHC design should advance predictive understanding of TCR specificity with implications for basic immunology and clinical diagnostics.

Special thanks to Joe Watson and Nate Bennett for help with the fine-tuned RFab parameters (https://github.com/RosettaCommons/RFantibody), to the Alphafold2 developers for making their code and parameter sets freely available (https://github.com/google-deepmind/alphafold), to the creators of the SAbDab database (https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab) for sharing parsed antibody information, and to all the structural biologists whose work (https://www.rcsb.org/) makes structure-based approaches like this possible.

Please contact Phil Bradley with questions: pbradley at fredhutch dot org
DOI: 10.26008/1912/bco-dmo.985428.2
title: Radionuclides sediment cores collected on the R/V Thomas G. Thompson cruise TT013 in the Equatorial Pacific in 1992 during the U.S. JGOFS Equatorial Pacific (EqPac) project

description: This dataset reports measurements of radionuclides detected in sediment core samples collected on the R/V Thomas G. Thompson cruise TT013 in the Equatorial Pacific in 1992 during the U.S. JGOFS Equatorial Pacific (EqPac) project.
DOI: 10.15468/dl.mznb3h
title: Occurrence Download

description: A dataset containing 790 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample)",
 "Geometry POLYGON((-98.20346 17.97855,-79.54746 17.97855,-79.54746 31.27591,-98.20346 31.27591,-98.20346 17.97855))",
 "OccurrenceStatus is Present",
 "TaxonKey is Aetobatus narinari",
 "Year 2005-2025"
 ]
}

 The dataset includes 790 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001085-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dksjt5
title: Occurrence Download

description: A dataset containing 305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.8334 38.40089,-87.39413 33.22135,-88.63074 31.88904,-84.44435 31.70947,-77.48496 36.35007,-76.84494 37.41075,-78.8334 38.40089))",
 "TaxonKey is Arthraxon"
 ]
}

 The dataset includes 305 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001086-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ux9ru5
title: Occurrence Download

description: A dataset containing 1285943 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-105.80155 20.1309,-104.16007 20.1309,-104.16007 21.47851,-105.80155 21.47851,-105.80155 20.1309))",
 "TaxonKey is Chordata",
 "Year 2015-2025"
 ]
}

 The dataset includes 1285943 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001059-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7tk5un
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 5.86633",
 "DecimalLongitude is less than 5.86938955318439",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 26 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001088-251120083545085/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/13808350
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.3886/e127581v2-191697
title: Universal Access to Free School Meals and Student Achievement: Evidence from the Community Eligibility Provision

description: Replication materials for Ruffini, Krista. "Universal Access to Free School Meals and Student Achievement: Evidence from the Community Eligibility Provision." Journal of Human Resources. <br><br><b>A previously published version of this project contained 0 byte files. Please reference the latest version of the project to access the most current data. </b>
DOI: 10.15468/dl.d3b3um
title: Occurrence Download

description: A dataset containing 9300046 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is NMNH Extant Specimen Records (USNM, US)"
 ]
}

 The dataset includes 9300046 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001077-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17664302
title: "Reliability Analysis Dataset: MTSF vs Failure Rates

description: This dataset contains computed Mean Time to System Failure (MTSF) values and parameter sensitivity indices for a multi-component data-centre reliability model. The analysis examines how variations in the main server failure rate (α) affect overall system lifetime, while the storage failure rate (β = 0.025), network/cooling failure rate (γ = 0.020), and preventive maintenance rate (m = 0.050) are kept constant.

For each α value, the total transition rate λ₀ = α + β + γ + m is calculated, and the corresponding MTSF values are derived using the analytical reliability formulation presented in the associated manuscript. Sensitivity coefficients (S_α, S_β, S_γ, S_m) quantify the normalized effect of each parameter on the system’s reliability.

This dataset supports full reproducibility of the study’s reliability curves, including MTSF variation plots, reliability envelopes, and sensitivity profiles. It is intended for researchers working on reliability engineering, maintenance planning, data-centre performance evaluation, and stochastic or analytical modelling of complex infrastructure systems.
DOI: 10.5281/zenodo.17664303
title: "Reliability Analysis Dataset: MTSF vs Failure Rates

description: This dataset contains computed Mean Time to System Failure (MTSF) values and parameter sensitivity indices for a multi-component data-centre reliability model. The analysis examines how variations in the main server failure rate (α) affect overall system lifetime, while the storage failure rate (β = 0.025), network/cooling failure rate (γ = 0.020), and preventive maintenance rate (m = 0.050) are kept constant.

For each α value, the total transition rate λ₀ = α + β + γ + m is calculated, and the corresponding MTSF values are derived using the analytical reliability formulation presented in the associated manuscript. Sensitivity coefficients (S_α, S_β, S_γ, S_m) quantify the normalized effect of each parameter on the system’s reliability.

This dataset supports full reproducibility of the study’s reliability curves, including MTSF variation plots, reliability envelopes, and sensitivity profiles. It is intended for researchers working on reliability engineering, maintenance planning, data-centre performance evaluation, and stochastic or analytical modelling of complex infrastructure systems.
DOI: 10.15468/dl.tacggb
title: Occurrence Download

description: A dataset containing 682040 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "StateProvince is Huila"
 ]
}

 The dataset includes 682040 records from 490 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001047-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8brhh
title: Occurrence Download

description: A dataset containing 1551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Petrakia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1551 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001091-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tqwdkx
title: Occurrence Download

description: A dataset containing 51665394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0)"
 ]
}

 The dataset includes 51665394 records from 1559 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001071-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ax9efj
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Machine Observation",
 "TaxonKey is Indopiptadenia Brenan"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006960-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15546539
title: Attack Stages Inference

description: Traces of observation and reward machines relatives to simulated cyberattacks gathered from RL agents
DOI: 10.15468/dl.6brxk6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.014 -1.454819, 102.943714 -1.460385, 102.875158 -1.476945, 102.810019 -1.504093, 102.749899 -1.541159, 102.696278 -1.587232, 102.650477 -1.641178, 102.613623 -1.701669, 102.586624 -1.767217, 102.570146 -1.836207, 102.564595 -1.906941, 102.57011 -1.977678, 102.586555 -2.046676, 102.613528 -2.112237, 102.650365 -2.172744, 102.696161 -2.226708, 102.749787 -2.272799, 102.809924 -2.309881, 102.875089 -2.337041, 102.943678 -2.35361, 103.014 -2.359178, 103.084322 -2.35361, 103.152911 -2.337041, 103.218076 -2.309881, 103.278213 -2.272799, 103.331839 -2.226708, 103.377635 -2.172744, 103.414472 -2.112237, 103.441445 -2.046676, 103.45789 -1.977678, 103.463405 -1.906941, 103.457854 -1.836207, 103.441376 -1.767217, 103.414377 -1.701669, 103.377523 -1.641178, 103.331722 -1.587232, 103.278101 -1.541159, 103.217981 -1.504093, 103.152842 -1.476945, 103.084286 -1.460385, 103.014 -1.454819))"},{"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/0002555-260119153935675/datasets/export for details.
DOI: 10.14272/ibjgvaofrskzpy-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1-chloro-4-ethenylsulfonylbenzene)
title: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/ibjgvaofrskzpy-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1-chloro-4-ethenylsulfonylbenzene)
title: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/ibjgvaofrskzpy-uhfffaoysa-n.1
title: 1-chloro-4-ethenylsulfonylbenzene
title: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2, and canonical SMILES descriptor[cheminf_000007]: C=CS(=O)(=O)c1ccc(cc1)Cl, and by the IUPAC name[cheminf_000107]: 1-chloro-4-ethenylsulfonylbenzene.
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-59653

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0001146 | 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
 
 CHMO:0000763 | attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR)
 
 CHMO:0002337 | electrospray ionisation quadrupole detection (ESI Q Trap)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/ibjgvaofrskzpy-uhfffaoysa-n/chmo0002337
title: electrospray ionisation quadrupole detection (ESI Q Trap) (1-chloro-4-ethenylsulfonylbenzene)
title: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2

description: dataset for electrospray ionisation quadrupole detection (ESI Q Trap)
DOI: 10.14272/ibjgvaofrskzpy-uhfffaoysa-n/chmo0000763
title: attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) (1-chloro-4-ethenylsulfonylbenzene)
title: InChI=1S/C8H7ClO2S/c1-2-12(10,11)8-5-3-7(9)4-6-8/h2-6H,1H2

description: dataset for attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR)