cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48436/1x7hj-p8814
title: Numerical data for "Origin of the anomalous Hall effect in Cr-doped RuO2"

description: This dataset contains the numerical data accompanying the "Origin of the anomalous Hall effect in Cr-doped RuO2" paper.Computations were performed using VASP 6.3.0, and the postprocessing necessary to obtain the band structures was done using the vaspkit package.The Data directory contains the necessary input files to reproduce the computations and the output files produced by the computations.The Plots directory contains the data and gnuplot scripts used to plot figures presented in the paper:



Fig. 1: energies and weighted average of local magnetic moments of various magnetic phases of Cr-doped RuO2 in the VCA approximation

Fig. 2: the absolute value of total magnetic moment with corresponding energy and local magnetic moment for a set of supercells of RuO2 with 20% Cr doping

Fig. 3: magnetic moment distribution for the two lowest-energy structures

Fig. 4: effective Curie-Weiss moments from Ref. [14] compared to a Cr-only model and anomalous Hall conductivity measured in Ref. [14] and its derivative with respect to the magnetic moment

Fig. 5: band-structure plots for the lowest-energy structure among the set of studied supercells


To produce the figure one simply runs plot.sh script in the respective directory: the script will use gnuplot and pdflatex to produce the plots.The data necessary to produce plots can be collected using the get.sh or get_data.sh scripts.In the case of VASP computations, the total energy is reported in the OUTCAR file at the line containing the free  energy   TOTEN  =  token, the local magnetization in the block after the magnetization token, and the total magnetic moment in the OSZICAR file at the line containing the mag=token. The input parameters are specified in the INCAR file and the k-grid mesh in KPOINTS file. The pseudopotentials should be obtained from the VASP distribution and saved as a single file, named POTCAR.

The following POTCARs were used for VASP calculations:md5sum                            name and locationb5c924befef4a180481bb6f65e4e516d  potpaw_LDA.54/Ru_pv/POTCAR6bfe8c9fc881319367b05c52d7f764ba  potpaw_LDA.54/Cr_pv/POTCARdd29215744b63de40827d7952527f753  potpaw_LDA.54/POTCAR

Licenses

The data is licensed under CC-BY, the code (scripts) are licensed under MIT.
DOI: 10.13026/e0kj-js78
title: Synthetic Mention Corpora for Disease Entity Recognition and Normalization

description: Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental
tasks in information extraction, particularly in the biomedical and clinical
domains. NER identifies textual mentions of entities, while EN maps these
mentions to unique identifiers within a structured vocabulary. However, the
biomedical domain presents unique challenges for NER, including the diverse
and inconsistent lexical representations of biomedical concepts, such as non-
standard terminology, abbreviations, complex phrases, and frequent
misspellings in clinical texts. Additionally, rare entities are often
underrepresented in training datasets and may lack detailed descriptions or
synonyms in knowledge graphs, limiting the quality of training data for
Disease Entity Recognition (DER) and Disease Entity Normalization (DEN). To
address this, we present the Synthetic Mention Corpora for Disease Entity
Recognition and Normalization, a dataset comprising 128,000 synthetic disease
mentions generated using a fine-tuned LLaMa-2-13B-Chat model. These mentions
are derived from the Unified Medical Language System (UMLS) disorder group.
This corpus aims to enhance the development of more robust systems for disease
entity identification and linking in biomedical and clinical text, addressing
current limitations in training data availability.
DOI: 10.13026/p5pn-ty93
title: Synthetic Mention Corpora for Disease Entity Recognition and Normalization

description: Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental
tasks in information extraction, particularly in the biomedical and clinical
domains. NER identifies textual mentions of entities, while EN maps these
mentions to unique identifiers within a structured vocabulary. However, the
biomedical domain presents unique challenges for NER, including the diverse
and inconsistent lexical representations of biomedical concepts, such as non-
standard terminology, abbreviations, complex phrases, and frequent
misspellings in clinical texts. Additionally, rare entities are often
underrepresented in training datasets and may lack detailed descriptions or
synonyms in knowledge graphs, limiting the quality of training data for
Disease Entity Recognition (DER) and Disease Entity Normalization (DEN). To
address this, we present the Synthetic Mention Corpora for Disease Entity
Recognition and Normalization, a dataset comprising 128,000 synthetic disease
mentions generated using a fine-tuned LLaMa-2-13B-Chat model. These mentions
are derived from the Unified Medical Language System (UMLS) disorder group.
This corpus aims to enhance the development of more robust systems for disease
entity identification and linking in biomedical and clinical text, addressing
current limitations in training data availability.
DOI: 10.15468/dl.d7ykh9
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((8.60463 3.97957,7.90157 3.1359,5.06589 -1.12933,5.55803 -1.92613,6.61262 -0.7075,8.88585 2.55002,9.19051 3.79209,8.60463 3.97957))",
 "OccurrenceStatus is Present",
 "TaxonKey is Asplenium sandersonii Hook."
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010813-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13026/5jes-bx23
title: Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

description: The task of Difference Visual Question Answering involves answering questions
about the difference between a pair of main and reference images. This process
is consistent with the radiologist's diagnosis practice that compares the
current image with the reference before concluding the report. We've assembled
a new dataset, called Medical-Diff-VQA, for this purpose. Unlike previous
medical VQA datasets, ours is the first one designed specifically for the
Difference Visual Question Answering task, with questions crafted to suit the
Assessment-Diagnosis-Intervention-Evaluation treatment procedure employed by
medical professionals. The Medical-Diff-VQA dataset, a derivative of the
MIMIC-CXR dataset, consists of questions categorized into seven categories:
abnormality (145,421), location (84,193), type (27,478), level (67,296), view
(56,265), presence (155,726), and difference(164,324). The 'difference'
questions are specifically for comparing two images. In total, the Medical-
Diff-VQA dataset contains 700,703 question-answer pairs derived from 164,324
pairs of main and reference images.
DOI: 10.5281/zenodo.14794696
title: CRAM Format DNA Data of an entity known as "eiritana" EIRI_CRAM.7z.002

description: CRAM data for the entity known as "eiritana"

7z.002

use with notification / attribution 

contact for archive pw
DOI: 10.5281/zenodo.14794697
title: CRAM Format DNA Data of an entity known as "eiritana" EIRI_CRAM.7z.002

description: CRAM data for the entity known as "eiritana"

7z.002

use with notification / attribution 

contact for archive pw
DOI: 10.15468/dl.m9hvy6
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is RBINS Mammal collection",
 "TaxonKey is Pan troglodytes (Blumenbach, 1775)"
 ]
}

 The dataset includes 75 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010815-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4huwgn
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is RBINS DaRWIN",
 "TaxonKey is Pan troglodytes (Blumenbach, 1775)"
 ]
}

 The dataset includes 77 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010816-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gds7p6
title: Occurrence Download

description: A dataset containing 4592 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Latrodectus mactans (Fabricius, 1775)",
 "Year 1980-2025"
 ]
}

 The dataset includes 4592 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010818-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5b39e8
title: Occurrence Download

description: A dataset containing 4041 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lutzomyia longipalpis (Lutz & Neiva, 1912)"
 ]
}

 The dataset includes 4041 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010817-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13348560
title: Understanding the Adoption of Modern JavaScript Features: An Empirical Study on Open-Source Systems

description: This repository contains the data and analysis from an empirical study investigating the adoption trends of modern JavaScript features introduced with ECMAScript 6 (ES6) and beyond. By mining the source code history of 158 open-source JavaScript projects, the study identifies efforts to rejuvenate legacy code by replacing outdated constructs with modern ones. The findings highlight the extensive use of modern features, their widespread adoption within one to two years after ES6's release, and ongoing trends in the rejuvenation of JavaScript codebases.





scripts.zip: Contains Python scripts used to analyze data and generate the graphs presented in the study's results.




jsminer-tool.zip: Includes the tool developed to analyze GitHub repository history and collect metrics on the adoption of modern JavaScript features.




jsminer_database_backup.zip: Provides a PostgreSQL database dump containing all code review comments from the repositories analyzed in the study.
DOI: 10.15468/dl.udrkgu
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Togo",
 "Geometry POLYGON((-35.99747 -23.79899,62.99747 -23.79899,62.99747 15.79899,-35.99747 15.79899,-35.99747 -23.79899))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Helichrysum mechowianum Klatt"
 ]
}

 The dataset's 1 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010809-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14794851
title: FQ Format DNA Data of an entity known as "eiritana" 7z.004

description: FQ data for the entity known as "eiritana"

7z.004

use with notification / attribution 

contact for archive pw
DOI: 10.5281/zenodo.14794852
title: FQ Format DNA Data of an entity known as "eiritana" 7z.004

description: FQ data for the entity known as "eiritana"

7z.004

use with notification / attribution 

contact for archive pw
DOI: 10.5517/ccdc.csd.cc2m2jz7
title: CCDC 2415519: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Georg Manolikakes|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2m2k09
title: CCDC 2415520: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Georg Manolikakes|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2m2k1b
title: CCDC 2415521: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Georg Manolikakes|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2m2k2c
title: CCDC 2415522: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Georg Manolikakes|2025|CSD Communication|||
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: 10.5517/ccdc.csd.cc2m2k3d
title: CCDC 2415523: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Georg Manolikakes|2025|CSD Communication|||
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: 10.5281/zenodo.14794593
title: CRAM Format DNA Data of an entity known as "eiritana" EIRI_CRAM.7z.001

description: CRAM data for the entity known as "eiritana"

7z.001

use with notification / attribution 

contact for archive pw
DOI: 10.5281/zenodo.14794594
title: CRAM Format DNA Data of an entity known as "eiritana" EIRI_CRAM.7z.001

description: CRAM data for the entity known as "eiritana"

7z.001

use with notification / attribution 

contact for archive pw
DOI: 10.17632/b4rbbx4grk.1
title: Supplementary material for the manuscript titled "Multi-heavy metal tolerant and ESBL (CTX-M-15)-positive Enterobacter kobei ST87 from oysters, Brazil."

description: Supplementary material for the manuscript titled "Multi-heavy metal tolerant and ESBL (CTX-M-15)-positive Enterobacter kobei ST87 from oysters, Brazil."

Supplementary text: Provides specific methodological details.

Table 1: Provides specific metadata on global Enterobacter kobei ST87 isolates.

Table 2: Presents a comparative SNP matrix analysis of global Enterobacter kobei ST87 isolates.
DOI: 10.5281/zenodo.14517343
title: ProteinCartography validation using deoxycytidine kinase accompanying data

description: This repository contains data accompanying the pub "Structure-based protein clustering sometimes, but not always, provides insight into protein function" (doi: https://doi.org/10.57844/arcadia-a757-3651). In the pub, we use the deoxycytidine kinase family to evaluate the results of a tool designed for structure-based clustering. We utilized data from the literature and data that we obtained for five enzymes (found in the table below).

There are three folders containing ProteinCartography data, enzyme activity analysis data, and purification data.

The ProtienCartography data contains all of the data produced by running ProteinCartography, including the maps, clustering information, and all of the structures used in the analysis. 

The enzyme activity analysis data contains enzyme activity data that we obtained in the lab, as well as data obtained from the review "Non-viral deoxyribonucleoside kinases - diversity and practical use" (doi: https://doi.org/10.1016/j.jgg.2015.01.003).

The purification data folder contains chromatograms, gels, and western blots from the purification of the following enzymes. 




UniProt ID
Annotation
Organism


A0A4Y1QVV5


P-loop containing nucleoside triphosphate hydrolases superfamily protein (pd-dNK)

Prunus dulcis (almond)


A0A3P6ASY1


Deoxynucleoside kinase domain-containing protein (bc-dNK)

Brassica campestris (field mustard)


A0A2A5BCG8


Deoxynucleoside kinase domain-containing protein (rb-dNK)

Unidentified Rickettsiales bacterium


A0A7J5YK87


Deoxynucleoside kinase domain-containing protein (dm-dCK)

Dissostichus mawsoni (Antarctic toothfish)


P27707
Deoxynucleoside kinase
Homo sapiens (human)
DOI: 10.5281/zenodo.14517344
title: ProteinCartography validation using deoxycytidine kinase accompanying data

description: This repository contains data accompanying the pub "Structure-based protein clustering sometimes, but not always, provides insight into protein function" (doi: https://doi.org/10.57844/arcadia-a757-3651). In the pub, we use the deoxycytidine kinase family to evaluate the results of a tool designed for structure-based clustering. We utilized data from the literature and data that we obtained for five enzymes (found in the table below).

There are three folders containing ProteinCartography data, enzyme activity analysis data, and purification data.

The ProtienCartography data contains all of the data produced by running ProteinCartography, including the maps, clustering information, and all of the structures used in the analysis. 

The enzyme activity analysis data contains enzyme activity data that we obtained in the lab, as well as data obtained from the review "Non-viral deoxyribonucleoside kinases - diversity and practical use" (doi: https://doi.org/10.1016/j.jgg.2015.01.003).

The purification data folder contains chromatograms, gels, and western blots from the purification of the following enzymes. 




UniProt ID
Annotation
Organism


A0A4Y1QVV5


P-loop containing nucleoside triphosphate hydrolases superfamily protein (pd-dNK)

Prunus dulcis (almond)


A0A3P6ASY1


Deoxynucleoside kinase domain-containing protein (bc-dNK)

Brassica campestris (field mustard)


A0A2A5BCG8


Deoxynucleoside kinase domain-containing protein (rb-dNK)

Unidentified Rickettsiales bacterium


A0A7J5YK87


Deoxynucleoside kinase domain-containing protein (dm-dCK)

Dissostichus mawsoni (Antarctic toothfish)


P27707
Deoxynucleoside kinase
Homo sapiens (human)
DOI: 10.13026/e6dd-cn74
title: Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

description: The task of Difference Visual Question Answering involves answering questions
about the difference between a pair of main and reference images. This process
is consistent with the radiologist's diagnosis practice that compares the
current image with the reference before concluding the report. We've assembled
a new dataset, called Medical-Diff-VQA, for this purpose. Unlike previous
medical VQA datasets, ours is the first one designed specifically for the
Difference Visual Question Answering task, with questions crafted to suit the
Assessment-Diagnosis-Intervention-Evaluation treatment procedure employed by
medical professionals. The Medical-Diff-VQA dataset, a derivative of the
MIMIC-CXR dataset, consists of questions categorized into seven categories:
abnormality (145,421), location (84,193), type (27,478), level (67,296), view
(56,265), presence (155,726), and difference(164,324). The 'difference'
questions are specifically for comparing two images. In total, the Medical-
Diff-VQA dataset contains 700,703 question-answer pairs derived from 164,324
pairs of main and reference images.
DOI: 10.13026/f2sv-v647
title: CXRGraph: Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation

description: CXRGraph is a dataset of structured radiology reports dataset following the
RadGraph format, which has been tailored for the Automatic Radiology Report
Generation (ARRG) task. CXRGraph assorts clinical information from full-text
radiology reports into five entity types and four relation types similar to
RadGraph. CXRGraph introduces three entity attributes, which are optionally
associated with an entity to provide additional information (e.g. abnormality)
and handle hallucinated prior references for the ARRG task. We manually
annotated the reports originally formatted in RadGraph, including 550 MIMIC-
CXR reports for model training and evaluation and 50 CheXpert reports for
evaluating the model generalization ability. By using the ground-truth data,
we developed a joint entity and relation model, achieving a micro-F1 of 96.6%
and 96.1% on named entity recognition, 94.0% and 89.8% on entity attribute
recognition, and 89.5% and 86.6% on relation extraction, on the MIMIC-CXR and
CheXpert test sets, respectively. Using the trained model, we automatically
annotated 227,835 MIMIC-CXR reports. Both the ground-truth and inference data
are available in CXRGraph. Given that the MIMIC-CXR and RadGraph have been de-
identified already, no protected health information (PHI) is included.
DOI: 10.13026/hbsg-tk87
title: Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

description: The task of Difference Visual Question Answering involves answering questions
about the difference between a pair of main and reference images. This process
is consistent with the radiologist's diagnosis practice that compares the
current image with the reference before concluding the report. We've assembled
a new dataset, called Medical-Diff-VQA, for this purpose. Unlike previous
medical VQA datasets, ours is the first one designed specifically for the
Difference Visual Question Answering task, with questions crafted to suit the
Assessment-Diagnosis-Intervention-Evaluation treatment procedure employed by
medical professionals. The Medical-Diff-VQA dataset, a derivative of the
MIMIC-CXR dataset, consists of questions categorized into seven categories:
abnormality (145,421), location (84,193), type (27,478), level (67,296), view
(56,265), presence (155,726), and difference(164,324). The 'difference'
questions are specifically for comparing two images. In total, the Medical-
Diff-VQA dataset contains 700,703 question-answer pairs derived from 164,324
pairs of main and reference images.