cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i24995835v2025n23p219
title: Co-progettare lo spazio al margine. I laboratori N.O.T.E. per l'empowerment di luoghi e comunità

description: Il progetto N.O.T.E. trasforma contesti urbani fragili in laboratori di esplorazione, partecipazione e cura. Attraverso drammatizzazioni, osservazione del territorio e pratiche collaborative, gli alunni hanno potuto esercitare agency, negoziare significati e attivare competenze socio-emotive e culturali. Il presente studio restituisce N.O.T.E. come dispositivo territoriale che genera apprendimento situato, comunità educanti e forme di cittadinanza condivisa.
description: The N.O.T.E. project transforms fragile urban contexts into spaces of exploration, participation and care. Through dramatization, field observation and collaborative practices, students exercised agency, negotiated meanings and activated socio-emotional and cultural competences. The present article shows N.O.T.E. as a territorial device that fosters situated learning, educating communities and shared forms of citizenship.
DOI: 10.25592/uhhfdm.18262
title: Bulk RNA-sequencing of murine bone marrow-derived macrophages upon treatment with live or apoptotic thymocyte-derived extracellular vesicles

description: To investigate the differential effects of extracellular vesicles (EVs) derived from live versus apoptotic cells on macrophage function, we performed bulk RNA sequencing on murine bone marrow-derived macrophages (BMDMs) treated with EVs isolated from live thymocytes (liveT-EVs) or apoptotic thymocytes (aT-EVs). BMDMs were generated from C57BL/6J mice and differentiated over seven days. On day 7, cells were treated for 24 hours with either liveT-EVs or aT-EVs. Apoptosis in donor thymocytes was induced by 24-hour culture following initial harvest, and EVs were isolated via differential ultracentrifugation. RNA was extracted using QIAshredder columns and the RNeasy mini Kit (Qiagen), with three biological replicates per condition.

Library preparation and transcriptome sequencing were performed by BGI using 100 bp paired-end reads on the DNBSEQ platform. Reads were aligned to the mouse reference genome GRCm39 using STAR aligner (version 2.7.10b). Differential gene expression analysis was performed using DESeq2 (version 1.40.2), and pathway analysis was conducted using clusterProfiler (version 4.8.3) and enrichR (version 3.4). This dataset reveals distinct transcriptomic signatures induced by EVs depending on the viability status of their donor cells, with apoptotic cell-derived EVs notably activating nitric oxide-related pathways in target macrophages.
DOI: 10.25592/uhhfdm.18261
title: Bulk RNA-sequencing of murine bone marrow-derived macrophages upon treatment with live or apoptotic thymocyte-derived extracellular vesicles

description: To investigate the differential effects of extracellular vesicles (EVs) derived from live versus apoptotic cells on macrophage function, we performed bulk RNA sequencing on murine bone marrow-derived macrophages (BMDMs) treated with EVs isolated from live thymocytes (liveT-EVs) or apoptotic thymocytes (aT-EVs). BMDMs were generated from C57BL/6J mice and differentiated over seven days. On day 7, cells were treated for 24 hours with either liveT-EVs or aT-EVs. Apoptosis in donor thymocytes was induced by 24-hour culture following initial harvest, and EVs were isolated via differential ultracentrifugation. RNA was extracted using QIAshredder columns and the RNeasy mini Kit (Qiagen), with three biological replicates per condition.

Library preparation and transcriptome sequencing were performed by BGI using 100 bp paired-end reads on the DNBSEQ platform. Reads were aligned to the mouse reference genome GRCm39 using STAR aligner (version 2.7.10b). Differential gene expression analysis was performed using DESeq2 (version 1.40.2), and pathway analysis was conducted using clusterProfiler (version 4.8.3) and enrichR (version 3.4). This dataset reveals distinct transcriptomic signatures induced by EVs depending on the viability status of their donor cells, with apoptotic cell-derived EVs notably activating nitric oxide-related pathways in target macrophages.
DOI: 10.25592/uhhfdm.18264
title: Supplementary Data: Colloidal synthesis of large near-bulk InAs quantum dots through seeded and seedless growth using cluster precursors

description: Data supporting the findings of the study: 'Colloidal synthesis of large near-bulk InAs quantum dots through seeded and seedless growth using cluster precursors'. Includes absorption, ATR-IR, XRD, TEM, HR-TEM, SAED, and SEM measurements. 
description: The project has received funding from the European Union`s Horizon 2020 research and innovation programme under grant agreement No 101016734.
DOI: 10.25592/uhhfdm.18263
title: Supplementary Data: Colloidal synthesis of large near-bulk InAs quantum dots through seeded and seedless growth using cluster precursors

description: Data supporting the findings of the study: 'Colloidal synthesis of large near-bulk InAs quantum dots through seeded and seedless growth using cluster precursors'. Includes absorption, ATR-IR, XRD, TEM, HR-TEM, SAED, and SEM measurements. 
description: The project has received funding from the European Union`s Horizon 2020 research and innovation programme under grant agreement No 101016734.
DOI: 10.15468/dl.m6wy2a
title: Occurrence Download

description: A dataset containing 5035 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-49.78069 -24.30801,-49.31956 -24.30801,-49.31956 -24.08031,-49.78069 -24.08031,-49.78069 -24.30801))",
 "HasGeospatialIssue is false",
 "Year 2015-2026"
 ]
}

 The dataset includes 5035 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000292-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/yqzk4r
title: PICNIC Baseline Data

description: PICNIC Baseline Data. Publication: Baseline metrics that may predict future myopia in young children
DOI: 10.5281/zenodo.18379638
title: Erosion of a cohesive granular bed by shear flow of non-cohesive particles

description: Data for the research study of erosion of a cohesive bed by granular flow
DOI: 10.5281/zenodo.18379640
title: Erosion of a cohesive granular bed by shear flow of non-cohesive particles

description: Data for the research study of erosion of a cohesive bed by granular flow
DOI: 10.6084/m9.figshare.31150846
title: <b>Predicted Biomass and Associated Uncertainty Maps for Mexico Based on Different Sampling Designs</b>

description: These rater files (GeoTiff) represent the modeling results of the following manuscript:Authors: Elena Díez-Pastor<sup>1,2</sup>, Huong Le<sup>1</sup>, José Armando Alanís-de la Rosa<sup>3</sup>, Metzli Ileana Aldrete-Leal<sup>3</sup>, Rubi Angélica Cuenca-Lara<sup>3</sup>, Sandra Liliana Medina-Casillas<sup>3</sup>, Taejin Park<sup>4,5</sup>, Rodrigo Ramos-Madrigal<sup>3</sup>, ​​Malinali Santacruz-Carrillo<sup>3</sup>, Elizabeth M. Smith<sup>1</sup>, Rodrigo Vargas<sup>1,2#</sup>Title: <b>Advancing Effective and Representative Sampling Designs for National Forest Monitoring</b>a <br><br><br>The abstract of such manuscript is:National forest inventories are crucial for assessing and monitoring forest resources, facilitating their effective management, and informing environmental policies. These efforts require substantial coordination, personnel, and financial resources, which are often limited, particularly in developing countries. Therefore, deciding where to establish inventory plots is essential for optimizing national forest monitoring designs. Using Mexico as an example, one challenge in completing their national forest inventory every five years is determining which forest plots to include while providing accurate data under unpredictable budget constraints. In collaboration with the <i>Comisión</i> <i>Nacional</i> <i>Forestal</i> (CONAFOR), this study develops a geostatistical framework using autocorrelated conditioned Latin Hypercube Sampling (<i>acLHS</i>) to optimize the design of the national forest inventory. We compared the baseline design (i.e., inventory from 2009-2014 with 19,251 plots) with alternative designs that retained 47%, 24%, and 12% of the original plots. We demonstrate that selecting fewer, yet representative, plots preserves univariate and multivariate probabilities, spatial dependencies, and upscaling capabilities for predicting forest biomass when using an optimization approach. The 24% design achieved 94.6% representativeness and predicted forest biomass with low error (1.22%) and uncertainty (1.50%) compared to the baseline design. Furthermore, the 24% design maintained data quality while enabling substantial potential reductions in sampling costs. This co-produced optimization approach demonstrates that the sample size can be reduced in budget-constrained scenarios while preserving geostatistical properties. Moreover, it is broadly applicable and customizable for any forest inventory and target variable (e.g., biomass, height, basal area), meeting reporting and budget expectations.
DOI: 10.6084/m9.figshare.31150846.v1
title: <b>Predicted Biomass and Associated Uncertainty Maps for Mexico Based on Different Sampling Designs</b>

description: These rater files (GeoTiff) represent the modeling results of the following manuscript:Authors: Elena Díez-Pastor<sup>1,2</sup>, Huong Le<sup>1</sup>, José Armando Alanís-de la Rosa<sup>3</sup>, Metzli Ileana Aldrete-Leal<sup>3</sup>, Rubi Angélica Cuenca-Lara<sup>3</sup>, Sandra Liliana Medina-Casillas<sup>3</sup>, Taejin Park<sup>4,5</sup>, Rodrigo Ramos-Madrigal<sup>3</sup>, ​​Malinali Santacruz-Carrillo<sup>3</sup>, Elizabeth M. Smith<sup>1</sup>, Rodrigo Vargas<sup>1,2#</sup>Title: <b>Advancing Effective and Representative Sampling Designs for National Forest Monitoring</b>a <br><br><br>The abstract of such manuscript is:National forest inventories are crucial for assessing and monitoring forest resources, facilitating their effective management, and informing environmental policies. These efforts require substantial coordination, personnel, and financial resources, which are often limited, particularly in developing countries. Therefore, deciding where to establish inventory plots is essential for optimizing national forest monitoring designs. Using Mexico as an example, one challenge in completing their national forest inventory every five years is determining which forest plots to include while providing accurate data under unpredictable budget constraints. In collaboration with the <i>Comisión</i> <i>Nacional</i> <i>Forestal</i> (CONAFOR), this study develops a geostatistical framework using autocorrelated conditioned Latin Hypercube Sampling (<i>acLHS</i>) to optimize the design of the national forest inventory. We compared the baseline design (i.e., inventory from 2009-2014 with 19,251 plots) with alternative designs that retained 47%, 24%, and 12% of the original plots. We demonstrate that selecting fewer, yet representative, plots preserves univariate and multivariate probabilities, spatial dependencies, and upscaling capabilities for predicting forest biomass when using an optimization approach. The 24% design achieved 94.6% representativeness and predicted forest biomass with low error (1.22%) and uncertainty (1.50%) compared to the baseline design. Furthermore, the 24% design maintained data quality while enabling substantial potential reductions in sampling costs. This co-produced optimization approach demonstrates that the sample size can be reduced in budget-constrained scenarios while preserving geostatistical properties. Moreover, it is broadly applicable and customizable for any forest inventory and target variable (e.g., biomass, height, basal area), meeting reporting and budget expectations.
DOI: 10.7910/dvn/wbwrn6
title: Replication Data for: Investment Functions with q in the Presence of Unobserved Persistent Shocks

description: Replication Data for "Investment Functions with q in the Presence of Unobserved Persistent Shocks" (accepted for publication at JFQA in 2026)
DOI: 10.15468/dl.m39m4u
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Propraopus sulcatus"
 ]
}

 The dataset includes 14 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000293-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5s3fs
title: Occurrence Download

description: A dataset containing 55367 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ursus americanus"
 ]
}

 The dataset includes 55367 records from 187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000272-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17250465
title: FII BCI Corpus P300

description: The FII BCI Corpus comprises a selection of BCI databases : P300 and Motor Imagery (MI), annotated and curated for research purposes in a collaborative project carried out at University Federico II of Naples and University Grenoble Alpes of Grenoble.

 

Besides using this page and Zenodo API, the corpus can be easily installed using Eegle's download GUI — see Eegle.Database.downloadDB.

The disk space requirements for the data here below is 14.2 GB.

 

Along with EEG data and class labels, the corpus provides comprehensive metadata that allow to select the data for the study at hand and extract relevant information. This makes it particularly easy to carry out machine learning research on BCI data — see for example Tutorial ML 2.

 

The data curation included:





discarding EEG recordings



flagged by the database authors as problematic

holding corrupted data (e.g., NaN values during the trials)

yielding numerical problems with standard data manipulations procedures

offering close-to-chance performance in standard 2-class prediction tasks,



conversion of data into µV with Float32 precision,

class re-labeling using a standardized scheme,

downsampling (if applicable) to ≤ 256 samples per second preventing aliasing,

removal of non-EEG channels, such as EOG, EMG, reference, or ground electrodes,

concatenation of runs from the same session with identical experimental condition,

cleaning of NaN and zero values at the beginning and ending of the recordings

conversion of the cleaned data from CSV to the NY format, easily accessible in any programming language.



For the details on the discarding procedures for each file see the @discarded.md file for MI and P300.

 

For all details on the construction of the corpus, see the FII BCI Corpus Overview.
DOI: 10.5281/zenodo.18379398
title: FII BCI Corpus P300

description: The FII BCI Corpus comprises a selection of BCI databases : P300 and Motor Imagery (MI), annotated and curated for research purposes in a collaborative project carried out at University Federico II of Naples and University Grenoble Alpes of Grenoble.

 

Besides using this page and Zenodo API, the corpus can be easily installed using Eegle's download GUI — see Eegle.Database.downloadDB.

The disk space requirements for the data here below is 14.2 GB.

 

Along with EEG data and class labels, the corpus provides comprehensive metadata that allow to select the data for the study at hand and extract relevant information. This makes it particularly easy to carry out machine learning research on BCI data — see for example Tutorial ML 2.

 

The data curation included:





discarding EEG recordings



flagged by the database authors as problematic

holding corrupted data (e.g., NaN values during the trials)

yielding numerical problems with standard data manipulations procedures

offering close-to-chance performance in standard 2-class prediction tasks,



conversion of data into µV with Float32 precision,

class re-labeling using a standardized scheme,

downsampling (if applicable) to ≤ 256 samples per second preventing aliasing,

removal of non-EEG channels, such as EOG, EMG, reference, or ground electrodes,

concatenation of runs from the same session with identical experimental condition,

cleaning of NaN and zero values at the beginning and ending of the recordings

conversion of the cleaned data from CSV to the NY format, easily accessible in any programming language.



For the details on the discarding procedures for each file see the @discarded.md file for MI and P300.

 

For all details on the construction of the corpus, see the FII BCI Corpus Overview.
DOI: 10.5281/zenodo.17228633
title: Structured Dengue Data for São José dos Campos, Ouro Preto, and Resende (2014–2024)

description: This dataset contains structured dengue epidemiological data for the Brazilian cities of São José dos Campos, Ouro Preto, and Resende, covering the period from 2014 to 2024. The data were collected from Info Dengue and SINAN (Sistema de Informação de Agravos de Notificação – Notifiable Diseases Information System) and processed according to structured data principles and data handling operations. As a result, four relations were generated: R1, R21, R22, and R23.

The dataset is part of an ongoing academic investigation, with further details and analyses to be presented in future publications. It is intended to support epidemiological research, data analysis, and the development of predictive models for dengue surveillance and control.

It is important to note that the dataset contains missing values in some attributes. Users are encouraged to apply pre-processing techniques, such as missing value imputation, normalization, or filtering, depending on the intended analysis.

The attributes of each relation are organized as follows:

Relation R1



epi_weeks: Epidemiological week (2014–2024).

casos: Number of reported cases.

avg_temp: Average temperature (°C).

avg_humid: Average relative humidity (%).


Relation R21



year: Year of record (2014-2024).

cases_autoc: Autochthonous cases.

cases_clinical: Clinical cases.

cases_laboratory: Laboratory-confirmed cases.


Relation R22



year: Year of record (2014-2024).

hospitalization: Indicator of hospitalization (1 = Yes, 0 = No).

quantity: Number of cases associated with hospitalization status.


Relation R23



year: Year of record (2014-2024).

status: Indicator of case outcome (1 = Recovered, 0 = Other)

quantity: Number of cases associated with the outcome.


 

Observation. An inconsistency was identified in the dataset because integer-valued data were incorrectly stored as floating-point values. Additionally, in Relation R23 for the city of Resende, data issues were also observed. These issues affected the dataset used in the analyses. The original source data remain unchanged. A new repository with the updated dataset will be made available.
DOI: 10.5281/zenodo.17228632
title: Structured Dengue Data for São José dos Campos, Ouro Preto, and Resende (2014–2025)
title: Data Science Approaches for Building Dengue Epidemiological Datasets

description: This repository contains structured dengue epidemiological data for the Brazilian cities of São José dos Campos, Ouro Preto, and Resende, covering the period from 2014 to 2025. The data were collected from InfoDengue and SINAN (Sistema de Informação de Agravos de Notificação – Notifiable Diseases Information System) and processed according to structured data principles and data handling operations. As a result, four relations were generated: R1, R21, R22, and R23.

The dataset is part of an ongoing academic investigation, with further details and analyses to be presented in future publications. It is intended to support epidemiological research, data analysis, and the development of predictive models for dengue surveillance and control.

It is important to note that the dataset contains missing values in some attributes. Users are encouraged to apply pre-processing techniques, such as missing value imputation, normalization, or filtering, depending on the intended analysis.

The attributes of each relation are organized as follows:

Relation R1



epi_weeks: Epidemiological week (2014–2025).

cases: Number of dengue case notifications reported in a given epidemiological week.

avg_temp: Average temperature (°C).

avg_humid: Average relative humidity (%).


Relation R21



year: Year of record (2014-2025).

cases_autoc: Autochthonous cases.

cases_clinical: Clinical cases.

cases_laboratory: Laboratory-confirmed cases.


Relation R22



year: Year of record (2014-2025).

hospitalization: Indicator of hospitalization (1 = Yes, 0 = No).

quantity: Number of cases associated with hospitalization status.


Relation R23



year: Year of record (2014-2025).

status: Indicator of case outcome (1 = Recovered, 0 = Deceased)

quantity: Number of cases associated with the outcome.


 

Notes and Updates





Data for 2025 were collected up to epidemiological week 50.




InfoDengue and SINAN continuously updates historical records due to delayed notifications; therefore, case counts may differ in future versions of the database.




The dataset was corrected: all relevant variables are now stored as integer values, ensuring consistency with the original data source.




A specific correction was applied to R23 for the city of Resende.
DOI: 10.15468/dl.rvcg49
title: Occurrence Download

description: A dataset containing 324931 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Specimen)",
 "Geometry POLYGON((-107.61 48.77,-83.69 48.77,-83.69 60.16,-107.61 60.16,-107.61 48.77))",
 "HasCoordinate is true",
 "License is one of (CC-BY-NC 4.0, CC-BY 4.0, CC0 1.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 324931 records from 278 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000247-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18379627
title: Exploring the Capabilities of Vision-Language Models to Detect Visual Bugs in HTML5  Applications (v2)

description: Data for the replication package for the paper "Exploring the Capabilities of VLMs to Detect Visual Bugs in HTML5 <canvas> Applications" (v2)




"Exploring the Capabilities of VLMs to Detect Visual Bugs in HTML5 <canvas> Applications"

by Finlay Macklon, Cedric Boucher and Cor-Paul Bezemer




Paper pre-print: ...

Code on GitHub: https://github.com/asgaardlab/vlm_canvas_bugs-code

Repositories containing <canvas> applications: vlm-canvas-bugs-external-repos GitHub account


See the following link on instructions for how to use this data in the replication package: https://github.com/asgaardlab/vlm_canvas_bugs-code

Directory structure:

Data
    ├── 1a-Collecting_Canvas_Applications
    ├── 1b-Creating_E2E_Test_Scripts (empty)
    ├── 1c-Injecting_Visual_Bugs
    ├── 1d-Collecting_Screenshots
    ├── 2-Experiments
    └── 3-Results