cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17544898
title: Cultural Keystone Species: A dataset of occurrences in English-language literature

description: We conducted a systematic literature review of the term "cultural keystone species" in English-language peer-reviewed publications indexed in Google Scholar. The initial search, conducted in January 2023 and covering documents up to December 2022, identified 175 publications reporting 699 CKS. Two subsequent updates were performed using the same protocol: one in early 2024 (including literature up to December 2023), which added 65 publications and 268 CKS, and another in early 2025 (up to December 2024), which added 68 publications and 203 CKS. In total, the three rounds of review resulted in 308 publications containing 1,170 CKS reports. 

CKS are represented as a species–sociocultural group binome. From each paper that used the term "cultural keystone species", we extracted information on the species considered culturally keystone and the sociocultural group that considered it culturally keystone. When the group was not explicitly stated, we inferred it from the context (e.g., using geographical location as a proxy for the sociocultural group).

The dataset includes the article's reference, year of publication, title, journal (if available), and DOI (if available). The dataset describes cultural keystone species by identifying their scientific names, levels of identification, families, classes, and kingdoms, as well as the sociocultural group language and the continent. The scientific names were checked against “Plants of the World Online” in case of plants,” Catalogue of Life” in case of animals, and Index Fungorum in case of fungi. For all articles published by the end of 2023, the Nature's Contributions to People (Diaz et al. 2018) and Stewardship Practices (Mattalia et al. forthcoming) associated with each CKS described in the literature were coded and included in the dataset.
DOI: 10.5281/zenodo.17544897
title: Cultural Keystone Species: A dataset of occurrences in English-language literature

description: We conducted a systematic literature review of the term "cultural keystone species" in English-language peer-reviewed publications indexed in Google Scholar. The initial search, conducted in January 2023 and covering documents up to December 2022, identified 175 publications reporting 699 CKS. Two subsequent updates were performed using the same protocol: one in early 2024 (including literature up to December 2023), which added 65 publications and 268 CKS, and another in early 2025 (up to December 2024), which added 68 publications and 203 CKS. In total, the three rounds of review resulted in 308 publications containing 1,170 CKS reports. 

CKS are represented as a species–sociocultural group binome. From each paper that used the term "cultural keystone species", we extracted information on the species considered culturally keystone and the sociocultural group that considered it culturally keystone. When the group was not explicitly stated, we inferred it from the context (e.g., using geographical location as a proxy for the sociocultural group).

The dataset includes the article's reference, year of publication, title, journal (if available), and DOI (if available). The dataset describes cultural keystone species by identifying their scientific names, levels of identification, families, classes, and kingdoms, as well as the sociocultural group language and the continent. The scientific names were checked against “Plants of the World Online” in case of plants,” Catalogue of Life” in case of animals, and Index Fungorum in case of fungi. For all articles published by the end of 2023, the Nature's Contributions to People (Diaz et al. 2018) and Stewardship Practices (Mattalia et al. forthcoming) associated with each CKS described in the literature were coded and included in the dataset.
DOI: 10.5281/zenodo.17018307
title: An m/z- and intensity-based HRMS clustering algorithm targeted towards single-cell metabolomics

description: Single-cell (SC) metabolomics holds great potential in the development of novel diagnostic tools and mechanistic insights into cell biology. Using high-resolution mass spectrometry (HRMS), the masses of a single cell’s constituents can be determined with an accuracy high enough to derive their respective elemental compositions. Using a molecule’s mass and its MS fragmentation pattern, in many cases a molecular structure can be assigned or looked up in databases. Due to the small measurement cell volume of an Orbitrap MS instrument, samples are scanned multiple times, which necessitates across-scan clustering per sample, and across-sample alignment of m/z values. However, existing HRMS data processing software is not designed to process SC HRMS data, as it typically requires liquid chromatography retention times or reference spectra for m/z clustering and alignment. Furthermore, both binning and density-based clustering have their disadvantages, leading to both peak aggregation and peak splitting.- Herein, a novel, robust SC HRMS m/z clustering and alignment algorithm is presented and compared with two commercially available and industrially standard algorithms, Sciex MarkerView and Thermo FreeStyle. Furthermore, output is compared with clustering results from DBSCAN and binning. Global Clustering unTargeted Analysis (GCTA) enforces a strict maximum on the cluster size, thereby reducing the chance of peak aggregation, and allows for the preservation of sample information for subsequent compound backtracking. Across-scan clustering and across-sample alignment were contrasted for accuracy in finding peaks identified by commercial software output and peaks with known m/z values corresponding to standards and HMDB and LipidMaps database hits. Comparisons are made based on data recorded for quality control samples containing standard mixes as well as SC HRMS data recorded for two different cell lines. This work shows that the presented algorithm is comparable in accuracy with respect to MarkerView and FreeStyle, reliably identifies compounds, is less prone to peak splitting and successfully filters noise. Furthermore, it is shown to be competitive with DBSCAN, binning and MarkerView when compared to theoretical m/z values based on database hits. Lastly, when compared with binning approaches, GCTA is less sensitive to peak splitting while maintaining similar accuracy in clustering peaks. GCTA encompasses both m/z clustering and reference-free alignment, which makes it pivotal to further development of untargeted SC HRMS metabolomics.
DOI: 10.5281/zenodo.17018308
title: An m/z- and intensity-based HRMS clustering algorithm targeted towards single-cell metabolomics

description: Single-cell (SC) metabolomics holds great potential in the development of novel diagnostic tools and mechanistic insights into cell biology. Using high-resolution mass spectrometry (HRMS), the masses of a single cell’s constituents can be determined with an accuracy high enough to derive their respective elemental compositions. Using a molecule’s mass and its MS fragmentation pattern, in many cases a molecular structure can be assigned or looked up in databases. Due to the small measurement cell volume of an Orbitrap MS instrument, samples are scanned multiple times, which necessitates across-scan clustering per sample, and across-sample alignment of m/z values. However, existing HRMS data processing software is not designed to process SC HRMS data, as it typically requires liquid chromatography retention times or reference spectra for m/z clustering and alignment. Furthermore, both binning and density-based clustering have their disadvantages, leading to both peak aggregation and peak splitting.- Herein, a novel, robust SC HRMS m/z clustering and alignment algorithm is presented and compared with two commercially available and industrially standard algorithms, Sciex MarkerView and Thermo FreeStyle. Furthermore, output is compared with clustering results from DBSCAN and binning. Global Clustering unTargeted Analysis (GCTA) enforces a strict maximum on the cluster size, thereby reducing the chance of peak aggregation, and allows for the preservation of sample information for subsequent compound backtracking. Across-scan clustering and across-sample alignment were contrasted for accuracy in finding peaks identified by commercial software output and peaks with known m/z values corresponding to standards and HMDB and LipidMaps database hits. Comparisons are made based on data recorded for quality control samples containing standard mixes as well as SC HRMS data recorded for two different cell lines. This work shows that the presented algorithm is comparable in accuracy with respect to MarkerView and FreeStyle, reliably identifies compounds, is less prone to peak splitting and successfully filters noise. Furthermore, it is shown to be competitive with DBSCAN, binning and MarkerView when compared to theoretical m/z values based on database hits. Lastly, when compared with binning approaches, GCTA is less sensitive to peak splitting while maintaining similar accuracy in clustering peaks. GCTA encompasses both m/z clustering and reference-free alignment, which makes it pivotal to further development of untargeted SC HRMS metabolomics.
DOI: 10.5281/zenodo.17545293
title: Dataset for article: Current overview of artificial intelligence applied to multimodal images in optic neuropathies: scoping review
DOI: 10.5281/zenodo.17545294
title: Dataset for article: Current overview of artificial intelligence applied to multimodal images in optic neuropathies: scoping review
DOI: 10.26207/8cc4-q535
title: Data and code for "Structure and mechanics of cockroach antennae confer flexibility and shape strain transmission for proprioception"

description: A broad class of animals rely on touch sensation for perception.
Among insects, the American cockroach (Periplaneta americana)
uses a pair of soft antennae with distributed sensors to touch its
environment to guide decision making. During touch, forces on the
antenna can activate thousands of mechanosensors. To understand
the content of this sensory information, it is critical to understand
how antenna mechanics shape the transmission of contact forces.
Here, we investigated the mechanical behavior and morphology
of antennae of the American cockroach at the individual annulus
level through experiments, modeling, imaging with micro-computed
tomography (micro-CT), 3D reconstruction of antenna morphology
and finite element modeling (FEM). Our data and model predictions
revealed that the antenna flagellum bends according to a kinematic
chain model, with annuli connected by softer joints. Whereas
the middle region of the antenna consistently fractured under cyclic
bending, the tip region remained intact, revealing mechanical
specialization along the antenna. Micro-CT imaging revealed an
invagination of the exocuticle at annulus intersections of the tip. To
test the hypothesis that this structure could enhance flexibility and
robustness, we used FEM and confirmed that the invagination
allows for larger bending without structural failure (buckling). Applying
FEM to a morphologically accurate kinematic chain model of the
flagellum revealed the relationship between the local strain at the
location of campaniform sensilla, predicting the information available
for antenna proprioception. Taken together, these findings reveal
biomechanical adaptations of insect antennae and provide a critical
step toward a mechanistic understanding of touch sensation in a
touch specialist.
DOI: 10.15468/dl.wkhbrf
title: Occurrence Download

description: A dataset containing 709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((-7.07245 42.29564,-26.85018 42.11388,-26.85018 34.17739,-7.17061 34.70354,-7.07245 42.29564))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (c3ad790a-d426-4ac1-8e32-da61f81f0117, 2089ce96-4fb5-4a20-999c-3ccf45a27a4d, c58ac019-b413-4613-8772-39e57acbeb8e, bc092ff0-02e4-11dc-991f-b8a03c50a862, d3978a37-635a-4ae3-bb85-7b4d41bc0b88, c227aa75-e44d-42b3-89b5-3d2a6210c841, 1928bdf0-f5d2-11dc-8c12-b8a03c50a862, c98a8d0d-cab2-4506-8c0f-c91a5e4996b9, 5fa89f68-9af0-4a0d-8998-ea39695c1db9, dfad10f6-d84b-4fa2-b6d0-df2a3eb38b65, 28eb1a3f-1c15-4a95-931a-4af90ecb574d, c76cf030-2a95-11da-9cc1-b8a03c50a862, 0e1238eb-2c53-4ebe-a33f-0679b2e8c385, 54a7048f-6d1f-468e-bd38-a585e05ca24d, 2cd829bb-b713-433d-99cf-64bef11e5b3e, 19456090-b49a-11d8-abeb-b8a03c50a862, ccd1ddc0-6c21-11de-8224-b8a03c50a862, ada9d123-ddb4-467d-8891-806ea8d94230, b3bf3a83-1f68-4541-a03f-330d30d567f5, 1c9c3bb4-377c-4547-aa79-16ea755d6006, 1e9400fa-ce73-4737-9eaa-f25df148945c, 53f347a7-99ce-4528-a085-5c76eb1301a0, 6ba9a8cc-513a-4a51-bf93-6f5de8040a96, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f)",
 "TaxonKey is Scleralcyonacea"
 ]
}

 The dataset includes 709 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020219-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e239736
title: HIFLD OPEN Formerly Used Defense Sites (FUDS) Projects (Points)

description: FUDS FY22 and IRM Data. Property and Project geospatial data. Interim Risk Management Data. FUDS Program Division and District Boundaries.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.<br>
DOI: 10.3886/e239736v1
title: HIFLD OPEN Formerly Used Defense Sites (FUDS) Projects (Points)

description: FUDS FY22 and IRM Data. Property and Project geospatial data. Interim Risk Management Data. FUDS Program Division and District Boundaries.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.<br>
DOI: 10.15468/dl.rpwn6y
title: Occurrence Download

description: A dataset listing the 4098419 species recorded in GBIF matching the query:
{ }

 The dataset's 4098419 records were derived from 77606 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020204-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00011285
title: Identify treatment responders in patients with type 2 diabetes using a machine learning based dynamic cardiovascular risk assessment tool (ML-CVD) in clinical trials

description: Type 2 diabetes is a common health concern characterised by high blood sugar levels. It affects over 500 million adults worldwide and leads to serious health problems related to the heart. To manage diabetes, several types of medicine are now available. Sodium/glucose cotransporter 2 inhibitors (SGLT2i) help the kidneys get rid of extra sugar through urine and also protect the heart. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) help the body release more insulin after meals, lower blood sugar, and reduce appetite, which can also support weight loss and heart protection. Dipeptidyl peptidase-4 inhibitors (DPP4i) stop the breakdown of certain hormones that help the body make more insulin and reduce sugar release from the liver. Metformin mainly works by reducing the amount of sugar made by the liver and helping the body use insulin more effectively. Non-steroidal mineralocorticoid receptor antagonists (ns-MRA) block certain hormone signals that can damage the heart, lowering the risk of heart failure progression. Now clinical guidelines recommend special tools to figure out who might get cardiovascular events because of diabetes. The tools are designed to categorize patients into low, medium, or high cardiovascular risk, and the initiation of novel drugs is preferred for patients at high risk. However, these predictive tools weren’t always accurate enough for people with type 2 diabetes, especially for those already have heart diseases.
In our previous study, we created a new tool called ML-CVD (Machine Learning for CardioVascular Diseases). This tool used patient information and health updates over time to have a more precise prediction on the 5-year probability of heart disease than the traditional tools. It also helped to identify which patient benefit more from SGLT2i. But whether this model could predict the treatment effect of other drugs is unknown. In this study, we aim to validate the ML-CVD model in controlled clinical trials. We would estimate the risk score calculated by ML-CVD model and test the C-index (a popular statistic often applied to models with binary or survival outcome variables) of the model on predicting the risk in each trial to know if it's effective to predict risk for a broader patient group. Observing the change of the risk score, we would also identify those who might have more heart benefit from specific medications. The study could lead to better care for people with type 2 diabetes by providing more accurate risk assessments and identifying more beneficial treatments for each individual.
The study was transferred from the Vivli project ID00010038.
DOI: 10.6084/m9.figshare.30266302
title: pointpairs.json

description: This data represents the pairwise correspondence of single reflections traced via OptiX, specifically recording the point-to-point mapping relationships generated by single reflection events in the optical simulation process, which can support the verification and optimization of specular surface reconstruction algorithms.
DOI: 10.6084/m9.figshare.30266332
title: box.zip

description: This is the simulated data image I provided, generated using Blender.
DOI: 10.6084/m9.figshare.30266302.v1
title: pointpairs.json

description: This data represents the pairwise correspondence of single reflections traced via OptiX, specifically recording the point-to-point mapping relationships generated by single reflection events in the optical simulation process, which can support the verification and optimization of specular surface reconstruction algorithms.
DOI: 10.6084/m9.figshare.30266332.v1
title: box.zip

description: This is the simulated data image I provided, generated using Blender.
DOI: 10.5281/zenodo.15469269
title: novel_Cytoplasmic_Effectors-main
DOI: 10.5281/zenodo.15469270
title: novel_Cytoplasmic_Effectors-main
DOI: 10.5281/zenodo.17545285
title: Datasets of bootstrap readout using hidden references

description: Supplementary dataset description for “Fast bootstrap and reliable readout using hidden references for DNA data storage”

We provide the source data and sequencing data that support the findings of this study. The source data include original data files, encoded codewords, watermark sequences, and encoded sequences. Both real and simulated sequencing data were used to evaluate the performance of the proposed readout framework. Real sequencing data were obtained from Illumina HiSeq platforms (SE150 and SE250) and Oxford Nanopore (ONT) sequencing. Simulated sequencing data were generated using the ART read simulator. All real and simulated sequencing data are provided in FASTQ format.
DOI: 10.5281/zenodo.17545286
title: Datasets of bootstrap readout using hidden references

description: Supplementary dataset description for “Fast bootstrap and reliable readout using hidden references for DNA data storage”

We provide the source data and sequencing data that support the findings of this study. The source data include original data files, encoded codewords, watermark sequences, and encoded sequences. Both real and simulated sequencing data were used to evaluate the performance of the proposed readout framework. Real sequencing data were obtained from Illumina HiSeq platforms (SE150 and SE250) and Oxford Nanopore (ONT) sequencing. Simulated sequencing data were generated using the ART read simulator. All real and simulated sequencing data are provided in FASTQ format.
DOI: 10.15468/dl.b5qhby
title: Occurrence Download

description: A dataset containing 62739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pyrus communis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 62739 records from 986 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020202-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9h8gb4
title: Occurrence Download

description: A dataset containing 2740 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((-6.87843 42.25076,-27.38412 42.28549,-27.1495 35.57801,-6.73766 35.53984,-6.87843 42.25076))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (2089ce96-4fb5-4a20-999c-3ccf45a27a4d, 54a7048f-6d1f-468e-bd38-a585e05ca24d, 1e9400fa-ce73-4737-9eaa-f25df148945c, 2cd829bb-b713-433d-99cf-64bef11e5b3e, dfad10f6-d84b-4fa2-b6d0-df2a3eb38b65, c3ad790a-d426-4ac1-8e32-da61f81f0117, 53f347a7-99ce-4528-a085-5c76eb1301a0, c76cf030-2a95-11da-9cc1-b8a03c50a862)",
 "TaxonKey is one of (Milleporidae, Stylasteridae, Pennatulacea, Malacalcyonacea, Scleralcyonacea, Antipatharia, Scleractinia)"
 ]
}

 The dataset includes 2740 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020223-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.apn86w
title: Occurrence Download

description: A dataset containing 1676 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "NetworkKey is 99d66b6c-9087-452f-a9d4-f15f2c2d0e7e",
 "TaxonKey is Chordata"
 ]
 },
 "TaxonKey is one of (Glyptemys insculpta [GBIF Backbone Taxonomy], Glyptemys insculpta [GBIF Backbone Taxonomy])"
 ]
}

 The dataset includes 1676 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020221-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udvdyb
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Vriesea paraibica"
 ]
}

 The dataset includes 47 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020224-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7266/zj4phwx2
title: Citizen Science Water Quality Data
DOI: 10.15468/dl.rvdgwx
title: Occurrence Download

description: A dataset containing 143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lasiosiphon splendens"
 ]
}

 The dataset includes 143 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020225-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17545329
title: Neuroleptospirosis in patients presenting with primary central nervous system infection: a multicentre study