cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17660047
title: WeGA data package

description: The WeGA data package is a collection of TEI and MEI documents, constituting the digital edition of the Carl-Maria-von-Weber-Gesamtausgabe. The files are derived from our internal WeGA TEI format and are valid against TEI_all and mei_all respectively. WeGA element additions such as tei:workName are mapped to the more generic tei:name element, but no information was dropped. Internal references are provided via @ref attributes and full URIs, rather than magic @key attributes.


 


1. Contents


Every folder at the root level of the archive holds a distinctive WeGA document type



	
addenda: Addenda and Corrigenda concerning the printed volumes of the WeGA
	
biblio: bibliographic objects, forming our bibliography
	
diaries: Carl Maria von Weber's diaries
	
documents: various documents, not fitting any other category
	
letters: correspondence materials
	
news: website news, forming our "news" section on the website
	
orgs: organizations and institutions
	
persons: personographies
	
places: place descriptions
	
thematicCommentaries: small essays that comment on a collection of e.g. letters or diary entries
	
var: various website pages, e.g. API documentation
	
works: work descriptions
	
writings: writings, mostly reviews and historic news



 


2. License


The WeGA data package is released under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Enjoy!


 


3. Contact


This dataset is provided by the Carl-Maria-von-Weber-Gesamtausgabe


c/o Musikwissenschaftliches Seminar Detmold/Paderborn


Hornsche Str. 39


D–32756 Detmold


info at weber-gesamtausgabe.de


 


4. DOI


10.5281/zenodo.3520701
DOI: 10.15468/dl.vf4hka
title: Occurrence Download

description: A dataset containing 2117 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Succowia balearica"
 ]
}

 The dataset includes 2117 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000201-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3yqtj
title: Occurrence Download

description: A dataset containing 71468 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Anas crecca"
 ]
}

 The dataset includes 71468 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000197-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30665360.v1
title: supplementary materials for <i>Mapping the Disease Burden and Risk Factors of Type 2 Diabetes Among Women of Childbearing Age, </i><i>w</i><i>ith Modelled Projections to 2050</i>

description: This dataset contains the supplementary materials for the article <i>“Mapping the Disease Burden and Risk Factors of Type 2 Diabetes Among Women of Childbearing Age, with Modelled Projections to 2050.”</i><br>It includes detailed tables and figures supporting the analyses presented in the paper.
DOI: 10.6084/m9.figshare.30665360
title: supplementary materials for <i>Mapping the Disease Burden and Risk Factors of Type 2 Diabetes Among Women of Childbearing Age, </i><i>w</i><i>ith Modelled Projections to 2050</i>

description: This dataset contains the supplementary materials for the article <i>“Mapping the Disease Burden and Risk Factors of Type 2 Diabetes Among Women of Childbearing Age, with Modelled Projections to 2050.”</i><br>It includes detailed tables and figures supporting the analyses presented in the paper.
DOI: 10.34740/kaggle/dsv/13803502
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.15468/dl.mhsesu
title: Occurrence Download

description: A dataset containing 511 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scrophularia arguta"
 ]
}

 The dataset includes 511 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000202-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26093755
title: Lists of target proteins of MCLOW, WM.

description: The associations between acting proteins of MCLOW/WM and obesity-associated proteins.
DOI: 10.6084/m9.figshare.26093755.v4
title: Lists of target proteins of MCLOW, WM.

description: The associations between acting proteins of MCLOW/WM and obesity-associated proteins.
DOI: 10.6084/m9.figshare.26093755.v3
title: Lists of target proteins of MCLOW, WM.

description: The associations between acting proteins of MCLOW/WM and obesity-associated proteins.
DOI: 10.57903/uj/c9ns6v
title: From 2D to 3D: Automated Ultrasound Segmentation and Cross-Sectional Validation in Murine Tumor Models - replication data

description: The dataset comprises B-mode ultrasound images of subcutaneous murine tumors acquired in two preclinical models. Human LN229 glioblastoma multiforme cells were inoculated into the interscapular fat pad of female BALB/c AnN-Foxn1nu/nu/Rj mice, and mouse mammary carcinoma 4T1 cells were inoculated into the mammary fat pad of female BALB/cAnNRj mice (Janvier Labs, Le Genest-Saint-Isle, France). Anatomical B-mode images were acquired using a Vevo F2 ultrasound system with a 25–57 MHz transducer and a Vevo 2100 system with an MS-550D transducer (VisualSonics, Toronto, ON, Canada). All procedures were approved by the Second Local Ethics Committee of Cracow (Permission No. 165/2023 and 250/2020).

The original material consisted of (i) individual 2D B-mode images with corresponding expert-drawn tumor masks and (ii) ultrasound videos acquired during freehand examinations, with or without frame-wise annotations. From these videos we extracted individual frames and, where available, the corresponding segmentation contours. The training dataset ultimately used for model development contained 565 images from the first (static-image) source and 2,877 frames extracted from videos; among the latter, 986 frames were provided with tumor masks and 1,891 frames had no mask because either no tumor was visible or the presence of a tumor in that frame was deemed too uncertain by the expert. Images were randomly divided into training, validation, and test subsets for quantitative evaluation.

To better characterize model robustness and out-of-distribution behavior, we additionally assembled two curated test resources. First, we selected 10 challenging validation images (e.g. low signal-to-noise ratio, strong contrast heterogeneity within the ROI, or very small tumors) and obtained independent manual segmentations from an expert who was not involved in dataset generation and typically works with different tumor types. Second, we created a “special” testing dataset consisting of 258 masked images from two mice that were not included in the training cohort. For each of these animals, B-mode images were acquired at two time points and in both axial and sagittal planes. In one mouse, LN229 tumors were located directly under the skin rather than in the interscapular fat pad, introducing a tumor location and appearance not represented in the training data. Together, these components provide a diversified benchmark for murine B-mode ultrasound segmentation, spanning multiple tumor models, implantation sites, image qualities, and distribution shifts.
DOI: 10.5281/zenodo.17659734
title: Scene-level movie data from Amazon X-Ray in the US market combined with IMDb

description: Dataset for "Scene-level movie data from Amazon X-Ray in the US market combined with IMDb."
DOI: 10.5281/zenodo.17659733
title: Scene-level movie data from Amazon X-Ray in the US market combined with IMDb

description: Dataset for "Scene-level movie data from Amazon X-Ray in the US market combined with IMDb."
DOI: 10.15468/dl.b5uzws
title: Occurrence Download

description: A dataset containing 16348 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Loxodonta africana",
 "HasCoordinate is true",
 "MediaType is Image",
 "HasGeospatialIssue is false",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 16348 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000203-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc29bj28
title: CCDC 2125269: Experimental Crystal Structure Determination

description: Related Article: Tao Zhang, Shujun Liang, Shuai Jia, Haibo Cui, Xinyi Wang, Xiutang Zhang|2025|RSC Advances|15|17811|doi:10.1039/D5RA02269J
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: 10.15468/dl.te786k
title: Occurrence Download

description: A dataset containing 5819 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Philorhizus melanocephalus (Dejean, 1825)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5819 records from 191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008981-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h9gu6t
title: Occurrence Download

description: A dataset containing 15969 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capsella bursa-pastoris (L.) Medik.",
 "Country is Poland"
 ]
}

 The dataset includes 15969 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009448-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yn8y5t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Maulisia Parr, 1960",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((37.8208 19.3058,37.7519 19.8496,35.9648 19.8496,35.9648 10.8496,44.9648 10.8496,44.9648 11.0757,44.5079 10.8984,44.021 11.3535,43.8449 12.4869,43.4392 13.1773,42.7805 13.6576,42.3014 14.2854,41.9925 14.3935,41.4722 15.0272,40.477 15.6908,40.2575 16.0951,39.9024 16.3482,38.9748 18.3342,37.8208 19.3058))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009449-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ecn2ya
title: Occurrence Download

description: A dataset containing 700328 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calidris canutus (Linnaeus, 1758)"
 ]
}

 The dataset includes 700328 records from 709 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011573-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udtfuh
title: Occurrence Download

description: A dataset containing 1361571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2015",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 1361571 records from 1738 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011604-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5zh52
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is Parapoynx diminutalis"
 ]
}

 The dataset's 1 records were derived from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024353-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29944340
title: Datasets associated with the manuscript entitled "Pyrumthecina Infraorder Nov.: Revisiting the morphological diversity and identifying the phylogenetic home of Argynnia within Arcellinida (Tubulinea:Amoebozoa)"

description: Arcellinida is a diverse lineage of testate amoebae within Amoebozoa, whose evolutionary history has been clarified through phylogenomics. These efforts have led to a stable classification of the group into suborders and infraorders. However, several taxa, such as the genus <i>Argynnia</i>, remain unplaced due to ambiguous morphology and unresolved positions in single-gene phylogenies. In this study, we explore the diversity of <i>Argynnia</i> by presenting new records of species sampled across Brazil. To address its longstanding taxonomic uncertainty, we performed a phylogenomic analysis incorporating transcriptomic data from the Protist 10,000 Genomes isolate P10K-MW-000941 (P10K941). Through single-gene phylogenies, this isolate was shown to be closely related to <i>Argynnia</i>, which previously lacked genomic-level data. Here, we further confirm this relationship and present a 227-gene phylogenomic reconstruction that identifies the placement of P10K941 within Arcellinida, thereby resolving the phylogenetic placement of <i>Argynnia</i>. We obtained a curated, contaminant-free phylogenomic dataset for Arcellinida, including the P10K941, through the PhyloFisher workflow. Our analysis recovers P10K941 as a well-supported long-branch, sister to the infraorder Longithecina. Given this phylogenomic placement, along with Argynnia’s characteristic morphology and the consistent results of single-marker analyses placing it outside existing infraorders, we propose the Pyrumthecina infraorder novum and the family Argynniidae family novum to accommodate the P10K941 isolate and all <i>Argynnia</i> species. These findings resolve the phylogenetic position of <i>Argynnia</i> and allow for interpreting shell evolution and deep diversification patterns within Arcellinida. The recognition of Pyrumthecina also highlights the likely existence of deeply branching lineages among arcellinid taxa currently classified as <i>incertae</i><i> sedis</i>.
DOI: 10.6084/m9.figshare.29944340.v1
title: Datasets associated with the manuscript entitled "Pyrumthecina Infraorder Nov.: Revisiting the morphological diversity and identifying the phylogenetic home of Argynnia within Arcellinida (Tubulinea:Amoebozoa)"

description: Arcellinida is a diverse lineage of testate amoebae within Amoebozoa, whose evolutionary history has been clarified through phylogenomics. These efforts have led to a stable classification of the group into suborders and infraorders. However, several taxa, such as the genus <i>Argynnia</i>, remain unplaced due to ambiguous morphology and unresolved positions in single-gene phylogenies. In this study, we explore the diversity of <i>Argynnia</i> by presenting new records of species sampled across Brazil. To address its longstanding taxonomic uncertainty, we performed a phylogenomic analysis incorporating transcriptomic data from the Protist 10,000 Genomes isolate P10K-MW-000941 (P10K941). Through single-gene phylogenies, this isolate was shown to be closely related to <i>Argynnia</i>, which previously lacked genomic-level data. Here, we further confirm this relationship and present a 227-gene phylogenomic reconstruction that identifies the placement of P10K941 within Arcellinida, thereby resolving the phylogenetic placement of <i>Argynnia</i>. We obtained a curated, contaminant-free phylogenomic dataset for Arcellinida, including the P10K941, through the PhyloFisher workflow. Our analysis recovers P10K941 as a well-supported long-branch, sister to the infraorder Longithecina. Given this phylogenomic placement, along with Argynnia’s characteristic morphology and the consistent results of single-marker analyses placing it outside existing infraorders, we propose the Pyrumthecina infraorder novum and the family Argynniidae family novum to accommodate the P10K941 isolate and all <i>Argynnia</i> species. These findings resolve the phylogenetic position of <i>Argynnia</i> and allow for interpreting shell evolution and deep diversification patterns within Arcellinida. The recognition of Pyrumthecina also highlights the likely existence of deeply branching lineages among arcellinid taxa currently classified as <i>incertae</i><i> sedis</i>.
DOI: 10.5281/zenodo.15278638
title: BIOMAT-CellNER: A Biomaterials Domain-Specific Corpus for Named Entity Recognition of Cell Mentions

description: BIOMAT-CellNER Corpus

BIOMAT-CellNER stands for BIOMATerials Cell Named Entity Recognition. It is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of cell entity mentions from the scientific literature in the biomaterials domain. It focuses on the annotation of cell mentions, including both cell types (e.g., fibroblasts, stem cells) and cell lines (e.g., HeLa, MC3T3), particularly those that are studied in interaction with biomaterials in experimental settings. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. To ensure domain relevance and terminological coverage, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials, tissue engineering, and related fields. The abstracts were then manually annotated according to the predefined rules in the annotation guidelines.

The BIOMAT-CellNER corpus is one of four developed within the project and is divided into three subsets: a training set (750 documents), a test set (150 documents), and a validation set (100 documents), available in multiple formats, including brat, CSV and CoNLL.

This corpus is part of a broader initiative to support the development of an advanced, searchable biomaterials database with integrated analytical tools and digital advisors. It is also intended for use in training Named Entity Recognition (NER) models, enabling the automatic identification and extraction of cell mentions relevant to biomaterials research.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database
DOI: 10.5281/zenodo.15278637
title: BIOMAT-CellNER: A Biomaterials Domain-Specific Corpus for Named Entity Recognition of Cell Mentions

description: BIOMAT-CellNER Corpus

BIOMAT-CellNER stands for BIOMATerials Cell Named Entity Recognition. It is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of cell entity mentions from the scientific literature in the biomaterials domain. It focuses on the annotation of cell mentions, including both cell types (e.g., fibroblasts, stem cells) and cell lines (e.g., HeLa, MC3T3), particularly those that are studied in interaction with biomaterials in experimental settings. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. To ensure domain relevance and terminological coverage, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials, tissue engineering, and related fields. The abstracts were then manually annotated according to the predefined rules in the annotation guidelines.

The BIOMAT-CellNER corpus is one of four developed within the project and is divided into three subsets: a training set (750 documents), a test set (150 documents), and a validation set (100 documents), available in multiple formats, including brat, CSV and CoNLL.

This corpus is part of a broader initiative to support the development of an advanced, searchable biomaterials database with integrated analytical tools and digital advisors. It is also intended for use in training Named Entity Recognition (NER) models, enabling the automatic identification and extraction of cell mentions relevant to biomaterials research.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database
DOI: 10.7910/dvn/tfbpxd
title: Replication Data for: Childhood psychosocial stress is associated with endothelial dysfunction in young adulthood, potentially via a SIRT1 or oxidative stress related mechanism

description: Data set includes ACEs, FMD, SIRT1, and oxLDL.
DOI: 10.7910/dvn/mrcc5z
title: Replication Data for Why do civil war successor parties renounce violence?

description: Includes data and do file and codebook
DOI: 10.7910/dvn/xxhcdz
title: Replication Data for: Does Brown beat Biesiada? Name fluency and electoral success

description: Contains the replication data for the article: Does Brown beat Biesiada? Name fluency and electoral success.

The data and code needed to replicate all analyses in the paper in the appendix are included.
DOI: 10.7910/dvn/nnp6jg
title: Replication Data for: Personal Votes, Electoral Competitiveness of Parties, and Legislative Representation in Taiwan under SNTV.

description: Replication files for "Personal Votes, Electoral Competitiveness of Parties, and Legislative Representation in Taiwan under SNTV." published in Japanese Journal of Political Science
DOI: 10.7910/dvn/o7hhkb
title: Replication Data for: Who are Your Neighbors? The Role of Ideology and Decline of Geographic Proximity in the Diffusion of Policy Innovations

description: Reproduction files for the associated article.