cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29039256.v1
title: Comparison of Humphrey versus Compass Perimetry for Hemianopsia

description: Up to 57% of stroke patients experience visual defects. Visual restitution therapy post-stroke remains controversial, with some attributing improvements to adaptive eye scanning movements rather than true field augmentation. We compare Compass fundus-tracking perimetry (CMP), which compensates for eye movements, to the Humphrey Field Analyzer (HFA) for homonymous hemianopsia. Nine patients (mean age: 47) with homonymous hemianopsia on HFA and corresponding neuroimaging defect were prospectively tested on the same day using the HFA (24–2 SITA Fast) and CMP (24–2 ZEST fast). Reliability indices, mean deviation (MD), and visual field index (VFI for HFA; FDPI for Compass) were compared via median differences and the Wilcoxon signed-rank test. The CMP had a significantly lower MD (median difference: −0.33 dB, <i>p</i> = .02), significantly greater false negative rate (median difference: 27%, <i>p</i> = .04), and a significantly longer test duration (median difference: 87 seconds, <i>p</i> = .01) than HFA. However, no between-analyzer difference occurred for visual field index (median difference: 6.8%, <i>p</i> = .65), false positive rate (median difference: −2.8%, <i>p</i> = .18), CMP blind spot index and HFA fixation losses (median difference: 0, <i>p</i> = .79). Bland-Altman plots showed acceptable agreement, with a + 2.42 dB bias in MD favoring CMP. CMP offers real-time compensation for fixation losses but did not show a clinically significant advantage over HFA for homonymous hemianopsia.
DOI: 10.6084/m9.figshare.29405459
title: Apigenin and treating bladder cancer: a mini-review of the current literature

description: Bladder cancer (BC) is a significant global public health concern, affecting a large number of people worldwide. Therefore, discovering effective strategies for its prevention and treatment is crucial to minimise its impact. Apigenin is a flavonoid found in fruits and vegetables. Studies have shown its effectiveness in treating various types of cancer. This review summarises the mechanisms of action and therapeutic effects of apigenin in treating BC. According to our findings, apigenin affects various signalling pathways and numerous protein families, including p53, caspase, PARP, cytochrome c, Bcl-2, PI3K/Akt, ERK1/2, JNK, AP-1, NF-κB, MAPK, MEKK1, and TRAIL, leading to cell death and inhibition of proliferation and migration in cancer cells. Thus, apigenin may represent a promising therapeutic approach for BC. However, the encouraging preclinical data should be evaluated in clinical settings through further studies, with appropriate methods and enhanced drug delivery systems.
DOI: 10.6084/m9.figshare.29405459.v1
title: Apigenin and treating bladder cancer: a mini-review of the current literature

description: Bladder cancer (BC) is a significant global public health concern, affecting a large number of people worldwide. Therefore, discovering effective strategies for its prevention and treatment is crucial to minimise its impact. Apigenin is a flavonoid found in fruits and vegetables. Studies have shown its effectiveness in treating various types of cancer. This review summarises the mechanisms of action and therapeutic effects of apigenin in treating BC. According to our findings, apigenin affects various signalling pathways and numerous protein families, including p53, caspase, PARP, cytochrome c, Bcl-2, PI3K/Akt, ERK1/2, JNK, AP-1, NF-κB, MAPK, MEKK1, and TRAIL, leading to cell death and inhibition of proliferation and migration in cancer cells. Thus, apigenin may represent a promising therapeutic approach for BC. However, the encouraging preclinical data should be evaluated in clinical settings through further studies, with appropriate methods and enhanced drug delivery systems.
DOI: 10.5281/zenodo.15737439
title: MatSim Dataset and benchmark for one-shot visual materials and textures recognition

description: The MatSim Dataset and benchmark

Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.

MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).

Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

 

Benchmark_MATSIM.zip:  contain the benchmark made of real-world images as described in the paper

Dataset Generation Scripts.zip: Contain the Blender (4.1) Python scripts used for generating the datasetMatSim_object_train_split_1,2,3....zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.

MatSimTrainObjectsNearField_.zip Contain train sets with near fieldlight sources

MatSim_Vessels_Train_1,2,3....zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.*Note: these are subsets of the dataset; the full dataset can be found at:https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX

orhttps://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF
DOI: 10.25828/1pd6-2r47
title: Green Occupations Crosswalk Data 2024
DOI: 10.5281/zenodo.7390165
title: MatSim Dataset and benchmark for one-shot visual materials and textures recognition

description: The MatSim Dataset and benchmark

Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.

MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).

Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

 

Benchmark_MATSIM.zip:  contain the benchmark made of real-world images as described in the paper

Dataset Generation Scripts.zip: Contain the Blender (4.1) Python scripts used for generating the datasetMatSim_object_train_split_1,2,3....zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.

MatSimTrainObjectsNearField_.zip Contain train sets with near fieldlight sources

MatSim_Vessels_Train_1,2,3....zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.*Note: these are subsets of the dataset; the full dataset can be found at:https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX

orhttps://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF
DOI: 10.5281/zenodo.7390166
title: MatSim Dataset and benchmark for one-shot visual materials and textures recognition

description: The MatSim Dataset and benchmark

Lastest version

Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.

MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).

Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

 

Benchmark_MATSIM.zip:  contain the benchmark made of real-world images as described in the paper

MatSim_object_train_split_1,2,3.zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.

MatSim_Vessels_Train_1,2,3.zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.*Note: these are subsets of the dataset; the full dataset can be found at:https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX

orhttps://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF

Code: 

Up to date code for generating the dataset, reading and evaluation and trained nets can be found in this URL:https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net

Dataset Generation Scripts.zip: Contain the Blender (3.1) Python scripts used for generating the dataset, this code might be odl up to date code can be found here Net_Code_And_Trained_Model.zip:  Contain a reference neural net code, including loaders, trained models, and evaluators scripts that can be used to read and train with the synthetic dataset or test the model with the benchmark. Note code in the ZIP file is not up to date and contains some bugs For the Latest version of this code see this URL

Further documentation can be found inside the zip files or in the paper.
DOI: 10.15468/dl.bqpfcd
title: Occurrence Download

description: A dataset containing 12122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus brachyptera Engelm."
 ]
}

 The dataset includes 12122 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069069-250525065834625/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.29405387
title: Temperature and conditions for thermalization after canonical quenches

description: Source data of displayed figures
DOI: 10.6084/m9.figshare.29405387.v1
title: Temperature and conditions for thermalization after canonical quenches

description: Source data of displayed figures
DOI: 10.6084/m9.figshare.29405021
title: Diagrama Primas

description: bla bla
DOI: 10.6084/m9.figshare.29405021.v1
title: Diagrama Primas

description: bla bla
DOI: 10.34740/kaggle/dsv/12284065
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17909/9yy2-vw75
title: TOI-562b transmission spectrum

description: JWST data used to create the transmission spectrum of TOI-562 (GJ 357b) for COMPASS [Program 2512] (Adams-Redai)
DOI: 10.13140/rg.2.2.11961.08802
title: درع المدرب من قناة التعليم والتطوير الذاتي
DOI: 10.13140/rg.2.2.11436.80003
title: CHUNJ WAS PURE AND UNADULTERATED INDIAN HIGHER COMMAND INCOMPETENCE The Indian commander Harbaksh Singh had limited understanding of mountain warfare and had neglected area north of Kishan Ganga River.Harbaksh Singh’s grasp of basic tactics was so pathetic that he had neglected to occupy the highest feature on Chunj Ridge Point 9444
DOI: 10.13140/rg.2.2.13612.59522
title: Certificate of Best reviewer ‬in Afaq Cinemaia International Scientific Journal issue 10 شهادة افضل محكم في العدد العاشر من المجلة العلمية الدولية آفاق سينمائية
DOI: 10.13140/rg.2.2.17859.32803
title: Treatment of Infectious Disease and Immunocompromised Patients
DOI: 10.13140/rg.2.2.25723.64809
title: Streptomyces viridis actinomycetoma
DOI: 10.13140/rg.2.2.14618.57284
title: Geopolitical background of Russo Turkish war of 1768-74
DOI: 10.13140/rg.2.2.18603.16164
title: Russian Threat Perception 1768
DOI: 10.15468/dl.38h4q7
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aspergillus sydowii (Bainier & Sartory) Thom & Church"
 ]
}

 The dataset's 1 records were derived from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019629-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5zf9e
title: Occurrence Download

description: A dataset containing 28955 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Morpho Fabricius, 1807"
 ]
}

 The dataset includes 28955 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019647-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zj4682
title: Occurrence Download

description: A dataset containing 28955 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Morpho Fabricius, 1807"
 ]
}

 The dataset includes 28955 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019648-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fqt8n9
title: Occurrence Download

description: A dataset containing 20530477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2005-2025"
 ]
}

 The dataset includes 20530477 records from 269 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019632-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yrgcx
title: Occurrence Download

description: A dataset listing the 154 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Actualización de la taxonomía y distribución de los anfibios y reptiles de San Luis Potosí"
 ]
}

 The dataset's 154 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019631-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a7zueg
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Visitantes florales de Zamia incognita A.Lisdtr & Idárraga, en una población natural de Maceo, Antioquia"
 ]
}

 The dataset includes 45 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019652-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pjtjq
title: Occurrence Download

description: A dataset containing 12397 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "TaxonKey is Arceuthobium M.Bieb."
 ]
}

 The dataset includes 12397 records from 214 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019653-250127130748423/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.25153277.v1
title: MS- Untargeted Metabolomics for Octocorals - ECUADOR

description: This study explores octocorals in Ecuador’s Marine Protected Area El Pelado as part of a marine biodiscovery project. Using untargeted metabolomics, researchers successfully classified 12 species across five genera, distinguishing closely related species, particularly within <i>Muricea</i>. Key secondary metabolites, including terpenes, were identified. The findings confirm high octocoral diversity in the Tropical Eastern Pacific and underscore the value of metabolomics for species classification and marine biodiversity research.
DOI: 10.6084/m9.figshare.27855233.v2
title: Decentralized Learning of Quantile Regression: A Smoothing Approach

description: Distributed estimation has attracted a significant amount of attention recently due to its advantages in computational efficiency and data privacy preservation. In this article, we focus on quantile regression over a decentralized network. Without a coordinating central node, a decentralized network improves system stability and increases efficiency by communicating with fewer nodes per round. However, existing related works on decentralized quantile regression have slow (sub-linear) convergence speed. We propose a novel method for decentralized quantile regression which is built upon the smoothed quantile loss. However, we argue that the smoothed loss proposed in the existing literature using a single smoothing bandwidth parameter fails to achieve fast convergence and statistical efficiency simultaneously in the decentralized setting. We propose a novel quadratic approximation of the quantile loss using a big bandwidth for the Hessian and a small bandwidth for the gradient. Our method enjoys a linear convergence rate and has optimal statistical efficiency. Numerical experiments and real data analysis are conducted to demonstrate the effectiveness of our method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27855233.v1
title: Decentralized Learning of Quantile Regression: a Smoothing Approach

description: Distributed estimation has attracted a significant amount of attention recently due to its advantages in computational efficiency and data privacy preservation. In this article, we focus on quantile regression over a decentralized network. Without a coordinating central node, a decentralized network improves system stability and increases efficiency by communicating with fewer nodes per round. However, existing related works on decentralized quantile regression have slow (sub-linear) convergence speed. We propose a novel method for decentralized quantile regression which is built upon the smoothed quantile loss. However, we argue that the smoothed loss proposed in the existing literature using a single smoothing bandwidth parameter fails to achieve fast convergence and statistical efficiency simultaneously in the decentralized setting. We propose a novel quadratic approximation of the quantile loss using a big bandwidth for the Hessian and a small bandwidth for the gradient. Our method enjoys a linear convergence rate and has optimal statistical efficiency. Numerical experiments and real data analysis are conducted to demonstrate the effectiveness of our method.
DOI: 10.25420/britishmuseum.28309385
title: John P Cooper Consent Form

description: Consent form for project Principal Investigator John P Cooper
DOI: 10.25420/britishmuseum.28309391
title: Swarup Bhattacharyya Consent Form

description: Consent form for project Research Assistant Swarup Bhattacharyya
DOI: 10.15468/dl.ust9hd
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Visitantes florales de Geonoma undata Klotzsch, 1847 en poblaciones naturales del sur del Valle de Aburrá"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019656-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hjbq3e
title: Occurrence Download

description: A dataset containing 28955 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Morpho Fabricius, 1807"
 ]
}

 The dataset includes 28955 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019655-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14833541
title: Productivity-Susceptibility Analysis Results for 306 Marine Aquarium Fish

description: The results of a productivity-susceptibility analysis of 306 top traded marine aquarium trade species. Dataset includes species name, family, factor score, estimate level, IUCN status, and endemic status. Final productivity, suscpetibility, vulnerability scores, and sustainability cluster are included as well. 

For columns indicating factor level this notes if the data the score is based on was species (SL), genus (GL), or family level (FL). 

For the cluster column, a value of 1 indicates moderate vulnerability, a score of 2 indicates low vulnerability,  and 3 indicates high vulnerability. 

This data serves as the supplementary materials for our publication.
DOI: 10.15468/dl.pzywrb
title: Occurrence Download

description: A dataset listing the 62330 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset's 62330 records were derived from 477 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019641-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14573922
title: PAINT Corpus Metadata

description: SCAD-CMD: metadata of the corpus of Sinophone critical art discourse (SCAD) collected for the "PAINT" Action (i.e., SCAD-C). This CSV file records titles, authors, and other publication details of 100 selected texts and includes three topic tags for each text. This work was supported by the OP JAC Project “MSCA Fellowships at Palacký University I.” CZ.02.01.01/00/22_010/0002593, run at Palacký University in Olomouc, Czech Republic.
DOI: 10.5281/zenodo.14573923
title: PAINT Corpus Metadata

description: SCAD-CMD: metadata of the corpus of Sinophone critical art discourse (SCAD) collected for the "PAINT" Action (i.e., SCAD-C). This CSV file records titles, authors, and other publication details of 100 selected texts and includes three topic tags for each text. This work was supported by the OP JAC Project “MSCA Fellowships at Palacký University I.” CZ.02.01.01/00/22_010/0002593, run at Palacký University in Olomouc, Czech Republic.
DOI: 10.5281/zenodo.14536610
title: Hackathon 2025: Streamflow Forecasting and Water Resource Regulation

description: I. Data

The objective of this challenge is to develop a model to predict streamflow levels across different river basins, aiding in sustainable water management strategies. The provided dataset includes time-series observations of water flow at specific stations within the selected watersheds, with a prediction horizon of 4 weeks.

Spatial dimension:



Covers key French river basins (Adour-Garonne, Rhône-Mediterranean) and the Doce River basin in Brazil.

Data is available for 30 stations for training and 39 stations for evaluation for France and for Brazil we have 9 stations for training, 13 stations for evaluation.


Temporal dimension:



Training data spans from 1990 to 2003.

Evaluation/inference data spans from 2004 to 2009. The evaluation dataset consists of multiple time segments, including a 4-week historical period for streamflow and weather (temperature, ...), 1 week of weather forecast data, and a 4-week prediction horizon for streamflow.


Participants will have access to the following feature types:

Spatiotemporal Climate Variables:



Temperature at 2m above ground

Total precipitation

Evaporation rates

Soil moisture (volumetric soil water)


Geospatial Features:



Soil properties (bulk density, clay content, sand/silt ratio). Source: Soilgrids

Hydrological divisions (watersheds and sub-basins). Source: BD Carthage for France and SNIRH for Brazil

Altitude Digital Elevation Model (NASA SRTM). Source: NASA SRTM 30m

Dams positions in France. Source: Hydrographic nodes


All features are provided in formats suitable for direct analysis and integration. You can download all the data below.

For more details about the hackathon see Here --> https://www.codabench.org/competitions/4335/