cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48577/jpl.exzvnh
title: Modeling Radiation Pressure Accelerations: Earth Radiance Anisotropy, Spacecraft Shape and Global Sampling

description: In this study, we investigate the feasibility of a direct measurement of Earth’s Energy Imbalance (EEI) from space via radiation pressure accelerations. EEI represents one of the most challenging Earth observations for climate change research and quantifies the fundamental rate of global heating in response to radiative forcings and feedbacks. Using state-of-the-art mission design and orbit determination software, we simulate Sun’s and Earth’s radiative pressure impact on spacecrafts of different shapes. We derive the Earth radiation (shortwave and longwave) intercepted by the spacecraft from observed radiative fluxes (Clouds and Earth’s Radiant Energy System, CERES) and correct for radiance anisotropy using anisotropy factors derived from historical Earth Radiation Budget (ERB) missions. Anisotropy is a function of solar and satellite viewing geometry, as well as of scene type (surface and atmospheric properties) of each Earth element in view of the satellite at any point in time. In addition, we test the sensitivity of confounding forces, such as aerodynamic drag and YORP effects to spacecraft shape, and present preliminary results of a sampling study to quantify the bias of daily global mean net radiative flux associated with a sun-synchronous obit crossing Earth’s equator at 12pm local time. Our investigations and software development will inform instrument and mission requirements relevant to the accurate measurement of EEI.
DOI: 10.15468/dl.qvtke3
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.60322 7.17595,-74.35472 7.0025,-74.39071 7.26457,-74.60322 7.17595))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 137 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009363-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r1laum
title: Occurrence Download

description: A dataset containing 72185 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Belgian Coccinellidae - Ladybird beetles in Belgium"
 ]
}

 The dataset includes 72185 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002074-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48577/jpl.cxu9ea
title: UAV-BORNE BISTATIC SAR AND INSAR EXPERIMENTS IN SUPPORT OF STV AND SDC TARGET OBSERVABLES

description: The ongoing Distributed Aperture Radar Tomographic Sensors (DARTS) project at NASA Jet Propulsion Laboratory aims to mature and demonstrate multi-static SAR measurements for fine-scale 3D imaging of surface topography, vegetation, and surface deformation and change. The project explores the use of drones as SAR platforms and integrates software-defined radar on RF system-on-chip for compact and flexible radar instruments. This paper highlights the progress in DARTS hardware development, experiments, and data processing. The recent experiments have successfully demonstrated monostatic interferometry as well as acquisition and processing of bi-static SAR imagery. By leveraging the advantages of multi-static SAR and drone-based platforms, the project aims to build a testbed for future missions design and enhanced SAR imaging capabilities for scientific applications.
DOI: 10.15468/dl.9ttiv8
title: Occurrence Download

description: A dataset containing 1012 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bombus vosnesenskii Radoszkowski, 1862",
 "Country is United States of America",
 "DatasetKey is Bee Biology and Systematics Laboratory"
 ]
}

 The dataset includes 1012 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083389-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6amdnb
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "Country is Peru",
 "TaxonKey is Panthera onca (Linnaeus, 1758)"
 ]
}

 The dataset includes 55 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001186-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xvp7c9
title: Occurrence Download

description: A dataset containing 2201037 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-94.4688 15.99928,-91.95393 14.27878,-90.1596 16.31077,-91.45485 17.8912,-93.78759 17.42907,-94.4688 15.99928))"
 ]
}

 The dataset includes 2201037 records from 957 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132689-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60686/t-fsd3945
title: FSD3945 Political Life and Public Pedagogy in the Higher Music Education: Focus Group Interviews in Nordic Countries 2024
DOI: 10.48539/hbm449.nwvk.668
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Bone Marrow/Pelvis, v1.2

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts.
DOI: 10.48539/hbm252.ttqd.772
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Blood Vasculature, v1.8

description: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables (https://humanatlas.io/asctb-tables) aim to capture<br>anatomical human body parts, the typology of cells, and biomarkers used to identify cell types. The tables are<br>authored and reviewed by an international team of experts. A description of this Blood Vasculature ASCT+B table can be<br>found in "Anatomical Structures, Cell Types, and Biomarkers of the Healthy Human Blood Vasculature" by Boppana et al.<br>2023. The database and code used to generate the more simplified view of the Blood Vasculature in these ASCT+B tables<br>may be found here :<br>(https://github.com/hubmapconsortium/hra-vccf). <br><br>**Bibliography:** <br><br>* Boppana, Avinash, Sujin Lee, Rajeev Malhotra, Marc Halushka, Katherine S. Gustilo, Ellen M. Quardokus, Bruce W. Herr II, Katy Börner, and Griffin M. Weber. 2023. “Anatomical Structures, Cell Types, and Biomarkers of the Healthy Human Blood Vasculature.” *Scientific Data* 10 (1): 452. https://doi.org/10.1038/s41597-023-02018-0. <br>* Börner, Katy, Sarah A. Teichmann, Ellen M. Quardokus, James C. Gee, Kristen Browne, David Osumi-Sutherland, Bruce W. Herr II, et al. 2021. “Anatomical Structures, Cell Types and Biomarkers of the Human Reference Atlas.” *Nature Cell Biology* 23 (11): 1117–28. https://doi.org/10.1038/s41556-021-00788-6.
DOI: 10.48539/hbm437.jqvc.953
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Lymph Node, v1.2

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts. The lymph node ASCT+B<br>tables are derived from published literature, public datasets, and domain expertise from the authors. The table<br>provides a reference for a canonical human mesenteric lymph node; however, it is anticipated that these tables will be<br>amended to reflect lymph nodes draining other sites of the human body and changes arising from inflammation and other<br>perturbations. The gross anatomy of the lymph node was highly influenced by the following text:[(Willard-Mack<br>2006)](https://doi.org/10.1080/01926230600867727). Cell types and gene biomarkers derived from sequencing studies were<br>derived from the following studies:[(James et al. 2020)](https://doi.org/10.1038/s41590-020-0602-z), [(Xiang et al.<br>2020)](https://doi.org/10.3389/fcvm.2020.00052), and [(Szabo et al.<br>2019)](https://doi.org/10.1038/s41467-019-12464-3). Gene biomarkers for stromal cells were obtained from an orthogonal<br>study in mice (DOI: 10.1016/j.immuni.2018.04.006). Several studies employing flow cytometry and immunohistochemistry<br>aided in the identification of protein biomarkers. To support granular phenotyping, we list all reported biomarkers<br>including negative and differentially expressed proteins. We anticipate further refinement from multimodal studies<br>that pair sequencing and imaging studies from the same human sample. In total, this table reports 34 anatomical<br>structures, 45 cell types, and 223 biomarkers.
DOI: 10.48539/hbm723.qpqz.388
title: OMAP-17: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Thymus with IBEX, v1.0

description: OMAP-17 was designed for IBEX (Iterative Bleaching Extends multi-pleXity) imaging of fixed frozen human pediatric<br>thymus samples (https://doi.org/10.1038/s41596-921-00644-9; https://doi.org/10.1073/pnas.2018488117). The panel<br>consists of 9 cycles of 43 primary antibodies, 6 secondary antibodies, and the nuclear label Hoechst for image<br>alignment and nuclear segmentation. This OMAP provides a spatial context for 7 anatomical structures and more than 30<br>cell types and cell states (dividing, immature, precommitted) present in the ASCT+B thymus table<br>(https://doi.org/10.48539/HBM668.QPSF.678). This panel was specifically designed to phenotype unique, and often rare,<br>populations of thymic epithelial cells (TECs) of the medulla, e.g., myo-TECs and neuro-TECs. Marker selection was<br>guided by companion studies using advanced sequencing methods (scRNA-seq and CITE-seq) as well as expert review.<br>OMAP-17 is the first OMAP for the human thymus but targets several stromal and immune cell types included in OMAPs for<br>other lymphoid organs. OMAP-1 (lymph node; https://doi.org/10.48539/HBM458.NSWR.957), OMAP-11 (spleen;<br>https://doi.org/10.48539/HBM957.RHVB.545), and OMAP-10 (tonsil; (https://doi.org/10.48539/HBM663.JJMJ.628). This OMAP<br>does not contain markers for phenotyping plasma cells or ionocytes/tuft TECs. CD1a, a critical marker for developing<br>thymocytes, was removed from OMAP-17 due to steric hindrance of T cell markers in later cycles. Several antibodies<br>required custom conjugation by commercial suppliers or antibody conjugation kits (e.g., AF532 and iF594 conjugates).<br>This work is included in a spatial thymus atlas constructed by the Human Cell Atlas. Additional details on image<br>analysis, data integration, and the establishment of a common coordinate framework for the thymus can be found in the<br>accompanying paper (https://doi.org/10.1101/2023.10.25.562925). Note that three (3) of the antibodies have been<br>discontinued since this OMAP-17 was created: Novus Biologicals (catalog #:NBP2-54402PE) KRT10, Novus Biologicals<br>(catalog #: NBP2-75467) LY75 and this secondary antibody Thermo Fisher Scientific Z25303 AF532 conjugate.
DOI: 10.5281/zenodo.15865040
title: MetaGraph for FinNLP Research: A Large-Scale Knowledge Graph of GenAI in Financial NLP (2022–2025)

description: MetaGraph for FinNLP Research: A Large-Scale Knowledge Graph of GenAI in Financial NLP (2022–2025)

This dataset accompanies the paper "Prompting the Market? A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025)". It introduces MetaGraph, a machine-readable knowledge graph automatically constructed from 681 scientific papers (see note below) in the field of financial NLP. MetaGraph enables large-scale meta-analyses of research trends, conceptual relationships, and method co-usage in GenAI for finance.

Content Overview

MetaGraph represents the FinNLP research landscape as a structured graph where:



Nodes correspond to papers, models, datasets, tasks, and key research concepts.

Edges encode citation links, shared methods, topic similarity, and temporal co-evolution.


The graph facilitates:



Discovery of underexplored intersections in FinNLP research

Temporal analyses of GenAI adoption

Visual exploration of conceptual and methodological trends


Included Files



finnlp_ontology.json: Defines an extensible ontology of FinNLP concepts (e.g., model, dataset, task) and relation types.

finnlp_graph.graphml: The full MetaGraph in GraphML format, compatible with tools like Gephi, NetworkX, and GraphViz.

finnlp_graph.json: A simplified JSON version for human-readable access, including structured metadata for nodes and edges.


Use CasesThis dataset is intended for researchers in NLP, finance, and scientometrics. It can be used to:



Conduct bibliometric studies

Analyze the methodological evolution of GenAI in finance

Train or evaluate systems for scientific knowledge extraction or reasoning


CitationIf you use this resource, please cite the accompanying paper.

Note: 58 of the 681 papers used in our analysis are not included in the published dataset. These papers were posted to ArXiv under a CC BY-NC-ND 4.0 license, which prevents reusers from distributing any derivative, adapted form of the original material. As such, we will not be distributing them as part of the MetaGraph knowledge graph.
DOI: 10.5281/zenodo.16968876
title: MetaGraph for FinNLP Research: A Large-Scale Knowledge Graph of GenAI in Financial NLP (2022–2025)

description: MetaGraph for FinNLP Research: A Large-Scale Knowledge Graph of GenAI in Financial NLP (2022–2025)

This dataset accompanies the paper "Prompting the Market? A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025)". It introduces MetaGraph, a machine-readable knowledge graph automatically constructed from 681 scientific papers (see note below) in the field of financial NLP. MetaGraph enables large-scale meta-analyses of research trends, conceptual relationships, and method co-usage in GenAI for finance.

Content Overview

MetaGraph represents the FinNLP research landscape as a structured graph where:



Nodes correspond to papers, models, datasets, tasks, and key research concepts.

Edges encode citation links, shared methods, topic similarity, and temporal co-evolution.


The graph facilitates:



Discovery of underexplored intersections in FinNLP research

Temporal analyses of GenAI adoption

Visual exploration of conceptual and methodological trends


Included Files



finnlp_ontology.json: Defines an extensible ontology of FinNLP concepts (e.g., model, dataset, task) and relation types.

finnlp_graph.graphml: The full MetaGraph in GraphML format, compatible with tools like Gephi, NetworkX, and GraphViz.

finnlp_graph.json: A simplified JSON version for human-readable access, including structured metadata for nodes and edges.


Use CasesThis dataset is intended for researchers in NLP, finance, and scientometrics. It can be used to:



Conduct bibliometric studies

Analyze the methodological evolution of GenAI in finance

Train or evaluate systems for scientific knowledge extraction or reasoning


CitationIf you use this resource, please cite the accompanying paper.

Note: 58 of the 681 papers used in our analysis are not included in the published dataset. These papers were posted to ArXiv under a CC BY-NC-ND 4.0 license, which prevents reusers from distributing any derivative, adapted form of the original material. As such, we will not be distributing them as part of the MetaGraph knowledge graph.
DOI: 10.48539/hbm582.qbpd.334
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Fallopian Tube, v1.4

description: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables (https://humanatlas.io/asctb-tables) aim to capture<br>the nested fallopian-tube structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts. The Fallopian Tube<br>ASCT+B table includes detailed anatomical structures such as the infundibulum, ampulla, isthmus, and interstitial<br>segment, along with associated cell types like ciliated epithelial cells, secretory cells, and intercalary cells. This<br>comprehensive mapping supports research into reproductive biology and related medical conditions.
DOI: 10.48539/hbm485.lhdl.857
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Peripheral Nervous System, v1.1

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts.
DOI: 10.24403/jp.1497809
title: Erlebnis Industriekultur : Innovatives Thüringen seit 1800. - Katalog zur Ausstellung vom 6.6.-9.9.2018 in Pößneck, Julia Dünkel, Andreas Christoph (Hg.). - Pößneck : Stadt Pößneck, 2018
DOI: 10.5281/zenodo.14900870
title: Multiregional blood-brain barrier phenotyping identifies the prefrontal cortex as the most vulnerable region to ageing in mice - Dataset

description: This dataset consists of transmission electron microscopy (TEM) images of brain capillaries (diameter < 10 μm) from 23 C57Bl/6J mice. We analysed 12 young mice (6 female, 6 male) at 2-3-months of age and 11 aged mice (5 female, 6 male) at 18-20-months.

Images from three brain regions are included: corpus callosum (CC), hippocampus (HC), and the prefrontal cortex (PFC). Brain regions of interest were sectioned to ultrathin 60 nm sections for electron microscopy. Imaging magnification was chosen to show each vessel and adjecent perivascular area to include blood-brain barrier-relevant subcomponents. 

TEM images were segmented and analysed using FIJI (ImageJ). We include the orinal TEM images and the segmentation files used for analyses.

Reference DOI of the associated publication to be added.
DOI: 10.48539/hbm928.wtvj.677
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Main Bronchus, v1.0

description: With revision of the Lung v1.2-1.3 revisions, the Trachea and the Main Bronchus were split out into separate ASCT+B<br>tables as v1.0. With the excellent assistance of Joshua Fortriede, Ellen Quardokus and Aleix Puig Barbe (EBI), Uberon<br>and Cell Ontology were updated, terms added or revised where needed, AS-AS, AS-CT, CT-CT relationships corrected and<br>verified. The provided gene markers were reviewed and updated based on recent scRNAseq lung cell type references.
DOI: 10.48539/hbm243.zzgl.262
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Large Intestine, v1.3

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts.
DOI: 10.48539/hbm265.dqzw.372
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Uterus, v1.0

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts.
DOI: 10.48539/hbm792.npxx.335
title: 3D Reference Organ for Kidney, Male, Left, v1.0

description: [This reference organ](https://humanatlas.io/3d-reference-library) was created using data from the Visible Human Male,<br>provided by the National Library of Medicine.
DOI: 10.48539/hbm836.bdkq.873
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Lymph Vasculature, v1.4

description: The lymph vasculature table focuses on major central lymphatic structures. The primary source is Rouviere H. Anatomie<br>des Lymphatiques de l'Homme. Masson et Cie: Paris, 1932. This release extends lymphatic structures of the arm and<br>hand. Currently, “lymphatic endothelial cell” and “FLT4 (VEGFR-3)” are the only cell types and biomarkers included in<br>the table.
DOI: 10.15468/dl.3q43sj
title: Occurrence Download

description: A dataset containing 226564 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agrostis canina"
 ]
}

 The dataset includes 226564 records from 1290 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053571-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48539/hbm396.qvlq.449
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Lung, v1.2

description: [Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables](https://humanatlas.io/asctb-tables) aim to<br>capture the nested *part_of* structure of anatomical human body parts, the typology of cells, and biomarkers used to<br>identify cell types. The tables are authored and reviewed by an international team of experts. This “Single Lung Lobe”<br>table simplifies the ASCT+B table from Lung v1.1, to consider the anatomical and cellular composition of the lower<br>respiratory tract of one lung lobe as representative of the five human lung lobes, reducing table redundancies in<br>structure and cell nomenclature and to improve visualization by the ASCT+B Reporter.
DOI: 10.15468/dl.8w8d4j
title: Occurrence Download

description: A dataset containing 29193450 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((89.60498 35.76213,89.03759 11.93193,130.24398 19.44979,124.07366 41.64876,110.49096 38.49921,89.60498 35.76213))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 29193450 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053579-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48539/hbm525.vnxm.373
title: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) table for Muscular System, v1.3

description: Anatomical Structures, Cell Types, plus Biomarkers (ASCT+B) tables (https://humanatlas.io/asctb-tables) aim to capture<br>anatomical human body parts, the typology of cells, and biomarkers used to identify cell types. The tables are<br>authored and reviewed by an international team of experts. This ASCT+B table contains a hierarchical “partonomy” of<br>the muscular system. The top level anatomical structure (“AS/1”) is “muscular system” for all structures. The second<br>level (“AS/2”) divides the musculature into body regions. The third level (“AS/3”) lists muscle groups; and, the<br>fourth level (“AS/4”) lists individual muscles. Finally, the fifth level (“AS/5”) contains parts of muscles. This<br>version of the muscular system ASCT+B table only contains skeletal muscles. Future versions will add cardiac and<br>smooth muscles. Note that the main purpose of this table is to provide a comprehensive list of muscles and their<br>location in the body. Individual muscles might have their own ASCT+B table, which provides more detail about its<br>anatomical structure, cell types and biomarkers.