cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17771421
title: Data-set and analysis results_Social Media NGO Spain-Chile

description: Data-Set and results analysis of social media posts from 20 NGOs in Spain and Chile (May 2024).A mixed quantitative-qualitative methodology was used, combining the quantitative analysis of 3,611 publications during a month in social media of 20 NGOs from Spain and Chile through Facebook, Instagram, YouTube, LinkedIn, TikTok and X, using a social analytics tool, together with the qualitative study through structured face-to-face interviews with the communication managers of the NGOs in the sample.
DOI: 10.15468/dl.3ggvtp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phytophthora fluvialis",
 "HasCoordinate is true",
 "Year is greater than or equal to 1980"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018379-251120083545085/datasets/export for details.
DOI: 10.15468/dl.73jqju
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhadinorhynchus trivandricus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018381-251120083545085/datasets/export for details.
DOI: 10.5683/sp3/sunhgq
title: Hermabessiere et al., __________ Supp Data

description: This is the supplementary data for the study at ELA in 2022 where we measured the additive concentrations and gene expression in yellow perch
DOI: 10.15468/dl.486qx6
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristimantis rubicundus"
 ]
}

 The dataset includes 128 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018380-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xk9xg
title: Occurrence Download

description: A dataset containing 136 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((68.86566 51.14367,67.65837 48.94223,69.73491 46.75515,73.69482 46.92032,75.74721 48.73563,74.05701 50.83971,68.91395 51.21935,68.86566 51.14367))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hemiptera",
 "Year 2015-2025"
 ]
}

 The dataset includes 136 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018382-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17771503
title: Honest bees increase recruitment and gain confidence over time - Dataset

description: Data for submission for publication
DOI: 10.5281/zenodo.17771502
title: Honest bees increase recruitment and gain confidence over time - Dataset

description: Data for submission for publication
DOI: 10.5281/zenodo.17771519
title: METABOLIK SINDROM KELIB CHIQISHINING ASOSIY SHART-SHAROITLARI

description: Metabolik sindrom (MS) - bu insulin qarshiligi, markaziy yog' to'planishi, dislipidemiya va gipertenziya kabi bir qator metabolik nuqsonlarni o'z ichiga olgan kompleks patologik holat bo'lib, yurak-qon tomir kasalliklari va 2-tip qandli diabet rivojlanish xavfini sezilarli darajada oshiradi. So'nggi o'n yillikda metabolik sindromning tarqalishi butun dunyo bo'ylab epidemik darajaga yetdi, bu global sog'liqni saqlash muammosiga aylanmoqda. Ushbu maqola metabolik sindromning kelib chiqishiga ta'sir etuvchi asosiy shart-sharoitlarni zamonaviy ilmiy nuqtai nazardan tahsil qiladi. Tadqiqotning maqsadi MS patogenezidagi asosiy omillarni, ularning o'zaro ta'sirini va potentsial profilaktika choralarini aniq belgilashdan iborat.

Metabolik sindromning rivojlanishi asosan ikkita asosiy omil - genetik moyillik va atrof-muhit omillarining murakkab o'zaro ta'siri bilan bog'liq. Insulin qarshiligi MS patogenezining markaziy patofiziologik mexanizmi hisoblanib, u bir qator metabolik o'zgarishlarning boshlanish nuqtasi vazifasini bajaradi. Yirik xalqaro tadqiqotlar shuni ko'rsatdiki, turli irqiy va etnik guruhlarda metabolik sindromning namoyon bo'lish xususiyatlari sezilarli farqlarga ega, bu genetik polimorfizmning muhim rolini ta'kidlaydi. Atrof-muhit omillari orasida gipodinamiya va ortiqcha kaloriyali ovqatlanish asosiy o'rinni egallaydi, bu esa organizm energiya balansining buzilishiga olib keladi.

So'nggi yillarda o'tkazilgan tadqiqotlar shuni ko'rsatdiki, mikrobiomning buzilishi, surunkali yallig'lanish, oksidativ stress va endotel disfunksiyasi kabi yangi patogenetik mexanizmlar ham metabolik sindromning rivojlanishida muhim rol o'ynaydi. Epigenetik o'zgarishlar - DNK metilatsiyasi, giston modifikatsiyasi va mikroRNKlar ifodasining o'zgarishi - atrof-muhit omillarining genotipga ta'sir qilish mexanizmi bo'lib xizmat qiladi va metabolik sindromning transgeneratsion uzatilishini ta'minlashi mumkin. Ushbu maqolada metabolik sindromning kelib chiqish sharoitlarini chuqur o'rganish orqali uning oldini olish va samarali davolash strategiyalarini ishlab chiqishga hissa qo'shish maqsad qilingan.
DOI: 10.5281/zenodo.17771520
title: METABOLIK SINDROM KELIB CHIQISHINING ASOSIY SHART-SHAROITLARI

description: Metabolik sindrom (MS) - bu insulin qarshiligi, markaziy yog' to'planishi, dislipidemiya va gipertenziya kabi bir qator metabolik nuqsonlarni o'z ichiga olgan kompleks patologik holat bo'lib, yurak-qon tomir kasalliklari va 2-tip qandli diabet rivojlanish xavfini sezilarli darajada oshiradi. So'nggi o'n yillikda metabolik sindromning tarqalishi butun dunyo bo'ylab epidemik darajaga yetdi, bu global sog'liqni saqlash muammosiga aylanmoqda. Ushbu maqola metabolik sindromning kelib chiqishiga ta'sir etuvchi asosiy shart-sharoitlarni zamonaviy ilmiy nuqtai nazardan tahsil qiladi. Tadqiqotning maqsadi MS patogenezidagi asosiy omillarni, ularning o'zaro ta'sirini va potentsial profilaktika choralarini aniq belgilashdan iborat.

Metabolik sindromning rivojlanishi asosan ikkita asosiy omil - genetik moyillik va atrof-muhit omillarining murakkab o'zaro ta'siri bilan bog'liq. Insulin qarshiligi MS patogenezining markaziy patofiziologik mexanizmi hisoblanib, u bir qator metabolik o'zgarishlarning boshlanish nuqtasi vazifasini bajaradi. Yirik xalqaro tadqiqotlar shuni ko'rsatdiki, turli irqiy va etnik guruhlarda metabolik sindromning namoyon bo'lish xususiyatlari sezilarli farqlarga ega, bu genetik polimorfizmning muhim rolini ta'kidlaydi. Atrof-muhit omillari orasida gipodinamiya va ortiqcha kaloriyali ovqatlanish asosiy o'rinni egallaydi, bu esa organizm energiya balansining buzilishiga olib keladi.

So'nggi yillarda o'tkazilgan tadqiqotlar shuni ko'rsatdiki, mikrobiomning buzilishi, surunkali yallig'lanish, oksidativ stress va endotel disfunksiyasi kabi yangi patogenetik mexanizmlar ham metabolik sindromning rivojlanishida muhim rol o'ynaydi. Epigenetik o'zgarishlar - DNK metilatsiyasi, giston modifikatsiyasi va mikroRNKlar ifodasining o'zgarishi - atrof-muhit omillarining genotipga ta'sir qilish mexanizmi bo'lib xizmat qiladi va metabolik sindromning transgeneratsion uzatilishini ta'minlashi mumkin. Ushbu maqolada metabolik sindromning kelib chiqish sharoitlarini chuqur o'rganish orqali uning oldini olish va samarali davolash strategiyalarini ishlab chiqishga hissa qo'shish maqsad qilingan.
DOI: 10.5281/zenodo.17687651
title: Predictive Modeling of Prehistoric Site Distribution in the Southeastern Sulawesi Karst Landscape Using MaxEnt
title: Model Prediksi Distribusi Situs Prasejarah di Landsekap Karst Tenggara Sulawesi menggunakan MaxEnt

description: This study applies a predictive modeling approach using the MaxEnt (Maximum Entropy) algorithm to estimate the distribution of prehistoric archaeological sites in the karst landscape of the southeastern arm of Sulawesi Island. This region, which includes the karst zones of Matarombeo, Mekongga, Tangkelemboke, and Sombori, holds high archaeological potential but remains largely unexplored. By integrating presence data from 64 previously recorded archaeological sites with six environmental variables—elevation, slope, aspect, hydrology, and geology the MaxEnt model produced an indicative map of potential archaeological site locations. The model’s output was validated through a field survey in North Kolaka Regency in December 2024, which successfully identified several new sites across various probability classes. The resulting data and maps offer significant potential for cultural heritage preservation, spatial planning, and further research in similar karst environments.
DOI: 10.5281/zenodo.17014902
title: Predictive Modeling of Prehistoric Site Distribution in the Southeastern Sulawesi Karst Landscape Using MaxEnt
title: Model Prediksi Distribusi Situs Prasejarah di Landsekap Karst Tenggara Sulawesi menggunakan MaxEnt

description: This study applies a predictive modeling approach using the MaxEnt (Maximum Entropy) algorithm to estimate the distribution of prehistoric archaeological sites in the karst landscape of the southeastern arm of Sulawesi Island. This region, which includes the karst zones of Matarombeo, Mekongga, Tangkelemboke, and Sombori, holds high archaeological potential but remains largely unexplored. By integrating presence data from 64 previously recorded archaeological sites with six environmental variables—elevation, slope, aspect, hydrology, and geology the MaxEnt model produced an indicative map of potential archaeological site locations. The model’s output was validated through a field survey in North Kolaka Regency in December 2024, which successfully identified several new sites across various probability classes. The resulting data and maps offer significant potential for cultural heritage preservation, spatial planning, and further research in similar karst environments.
DOI: 10.5281/zenodo.17770620
title: Extrusion-Based Primitive Fitting: RGB-D Occluded Object Dataset

description:  

This dataset contains 200 single-view RGB-D scenes of real household objects recorded using the onboard RGB-D camera of a TIAGo robot. The robot observes a tabletop workspace from an inclined top–down viewpoint, and each scene includes four real objects selected from a set of 12 instances. The objects are deliberately arranged to create natural partial occlusions between them, providing challenging conditions for single-view 3D perception.

The dataset supports research on shape and pose estimation, occlusion handling and primitive-based modeling. For each scene, we provide the corresponding ROS bag file (including point cloud, RGB, depth, and TF data), standalone color and depth images, 2D bounding boxes, object labels, and the estimated superellipsoid parameters obtained using our extrusion-based primitive fitting method.

All data are captured in real-world conditions using a physical TIAGo robot and real objects, with natural sensor noise, depth discontinuities, and occlusions. The fitted superellipsoid parameters included in the dataset are algorithmic estimates produced by the extrusion-based reconstruction and optimization pipeline, not ground-truth object geometry.

This dataset accompanies the methods presented in:Menendez, E.; Martínez, S.; Díaz-de-María, F.; Balaguer, C. (2024). *Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios*. Biomimetics, 9(2), 100. https://doi.org/10.3390/biomimetics9020100
DOI: 10.5281/zenodo.17770621
title: Extrusion-Based Primitive Fitting: RGB-D Occluded Object Dataset

description:  

This dataset contains 200 single-view RGB-D scenes of real household objects recorded using the onboard RGB-D camera of a TIAGo robot. The robot observes a tabletop workspace from an inclined top–down viewpoint, and each scene includes four real objects selected from a set of 12 instances. The objects are deliberately arranged to create natural partial occlusions between them, providing challenging conditions for single-view 3D perception.

The dataset supports research on shape and pose estimation, occlusion handling and primitive-based modeling. For each scene, we provide the corresponding ROS bag file (including point cloud, RGB, depth, and TF data), standalone color and depth images, 2D bounding boxes, object labels, and the estimated superellipsoid parameters obtained using our extrusion-based primitive fitting method.

All data are captured in real-world conditions using a physical TIAGo robot and real objects, with natural sensor noise, depth discontinuities, and occlusions. The fitted superellipsoid parameters included in the dataset are algorithmic estimates produced by the extrusion-based reconstruction and optimization pipeline, not ground-truth object geometry.

This dataset accompanies the methods presented in:Menendez, E.; Martínez, S.; Díaz-de-María, F.; Balaguer, C. (2024). *Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios*. Biomimetics, 9(2), 100. https://doi.org/10.3390/biomimetics9020100
DOI: 10.5281/zenodo.17771350
title: Multiplexed tissue immunofluorescence data from a clinical trial of gemcitabine and nab-paclitaxel with or without the VDR agonist paricalcitol for metastatic pancreatic cancer

description: This dataset contains processed multiplexed tissue immunofluorescence data from a clinical trial of gemcitabine and nab-paclitaxel with or without the VDR agonist paricalcitol for metastatic pancreatic cancer (Perez et al. Nature Cancer). Whole slide digital images of metastatic pancreatic biopsy specimens were acquired at 200x magnification using a PhenoImager HT system (Akoya Biosciences, Hopkinton, MA). A pathologist inspected each image after spectral unmixing to confirm the presence of viable cancer and to exclude tissue processing artifacts. Next, images were segmented into tumor epithelial and stromal areas using supervised machine learning, followed by cell detection and segmentation (inForm 2.6, Akoya Biosciences, Hopkinton, MA). Cell phenotypes were assigned via separately trained machine learning algorithms relying upon single cell cytomorphology and protein expression data. Two different multiplex immunofluoresence panels were used for this analysis.  One panel enabled the characterization of VDR protein expression, along with the identification of the following cell phenotypes: CD4+ T cells, CD8+ T cells, CD4-CD8- T cells, macrophages (CD68+ and/or CD163+), other immune cells (CD45+CD3-CD68-CD163-), and stromal cells . The second panel was used to identify fibroblasts based on αSMA and FAP positivity. Both panels included a pan-cytokeratin antibody to identify neoplastic tumor cells and DAPI to identify nuclei.
DOI: 10.15468/dl.6x3f4c
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phytophthora foliorum",
 "HasCoordinate is true",
 "Year is greater than or equal to 1980"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018383-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dtqnum
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Micracanthorhynchina chandrai",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018384-251120083545085/datasets/export for details.
DOI: 10.15468/dl.qscphj
title: Occurrence Download

description: A dataset containing 8664 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Salvator merianae"
 ]
}

 The dataset includes 8664 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018385-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fqfmuz
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristimantis ruedai"
 ]
}

 The dataset includes 65 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018386-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17456298
title: Dataset for "Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration"

description: Accelerating materials development requires quantitative linkages between processing, microstructure, and properties. In this work, we introduce a framework for mapping microstructure onto a low-dimensional material manifold that is parametrized by processing conditions. A key innovation is treating microstructure as a stochastic process, defined as a distribution of microstructural instances rather than a single image, enabling the extraction of material state descriptors that capture the essential process-dependent features. We leverage the manifold hypothesis to assert that microstructural outcomes lie on a low-dimensional latent space controlled by only a few parameters. Using phase-field simulations of spinodal decomposition as a model material system, we compare multiple microstructure descriptors (two-point statistics, chord-length distributions, and persistent homology) in terms of two criteria: (1) intrinsic dimensionality of the latent space, and (2) invertibility of the processing-to-structure mapping. The results demonstrate that distribution-based descriptors can recover a two-dimensional latent structure aligned with the true processing parameters, yielding an invertible and physically interpretable mapping between processing and microstructure. In contrast, descriptors that do not account for microstructure variability either overestimate dimensionality or lose predictive fidelity. The constructed material manifold is shown to be locally continuous, wherein small changes in process variables correspond to smooth changes in microstructure descriptors. This data-driven manifold mapping approach provides a quantitative foundation for microstructure-informed process design and paves the way toward closed-loop optimization of processing--structure--property relationships in an integrated materials engineering context.
DOI: 10.5281/zenodo.17456299
title: Dataset for "Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration"

description: Accelerating materials development requires quantitative linkages between processing, microstructure, and properties. In this work, we introduce a framework for mapping microstructure onto a low-dimensional material manifold that is parametrized by processing conditions. A key innovation is treating microstructure as a stochastic process, defined as a distribution of microstructural instances rather than a single image, enabling the extraction of material state descriptors that capture the essential process-dependent features. We leverage the manifold hypothesis to assert that microstructural outcomes lie on a low-dimensional latent space controlled by only a few parameters. Using phase-field simulations of spinodal decomposition as a model material system, we compare multiple microstructure descriptors (two-point statistics, chord-length distributions, and persistent homology) in terms of two criteria: (1) intrinsic dimensionality of the latent space, and (2) invertibility of the processing-to-structure mapping. The results demonstrate that distribution-based descriptors can recover a two-dimensional latent structure aligned with the true processing parameters, yielding an invertible and physically interpretable mapping between processing and microstructure. In contrast, descriptors that do not account for microstructure variability either overestimate dimensionality or lose predictive fidelity. The constructed material manifold is shown to be locally continuous, wherein small changes in process variables correspond to smooth changes in microstructure descriptors. This data-driven manifold mapping approach provides a quantitative foundation for microstructure-informed process design and paves the way toward closed-loop optimization of processing--structure--property relationships in an integrated materials engineering context.
DOI: 10.5281/zenodo.17020162
title: Anomalous gas sensing characteristics of TiO2 nanorod arrays irradiated with high-energy ion beam

description: The dataset contains the electrical resistance data for three TiO2 samples and input files to generate the first-principles calculation results.
DOI: 10.5281/zenodo.17020163
title: Anomalous gas sensing characteristics of TiO2 nanorod arrays irradiated with high-energy ion beam

description: The dataset contains the electrical resistance data for three TiO2 samples and input files to generate the first-principles calculation results.
DOI: 10.15468/dl.r4qjr5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paracanthocephaloides tripathii",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018389-251120083545085/datasets/export for details.
DOI: 10.15468/dl.k4rtta
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristimantis rufioculis"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018390-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.