cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10802196
title: KEANE (faKe nEws At seNtence lEvel) dataset.

description: This dataset is aimed at developing systems for detecting fake news. It contains links to articles, posts and other types of publications that have been previously evaluated by verifying entities. Sentence-level check-worthiness and fact-checking annotations are provided for each of these news items.
DOI: 10.5281/zenodo.10814704
title: Support for Baker's law: facultative self-fertilization ability decreases pollen limitation in experimental colonization

description: Support for Baker’s law: facultative self-fertilization ability decreases pollen limitation in experimental colonization (datasets for paper in the American Journal of Botany)

There are 2 files associated with this manuscript. The “PLseedsetindividual.csv” file contains seed set for the pollen limitation treatments for each plant in the experimental populations. Pollinator observation data is available in the “pollinator.csv” file

Description of the data and file structure:

PLseedsetindividual.csv



source.population = one of the four source populations used to create our experimental populations

autonomy= autonomous selfing category, high or low

individual = plant id

experimental.population = the id of the experimental population 

site = site ID

size = size of experimental population, single or small

date initiated = date experimental population was put in the field

dayfromstartofexperiment = derived from date, the time from the start of the experiment that the experimental population was initiated

treatment = the pollination treatment for that flower, control or supplemented

seed number = seed set for the treated flower


Pollinator.csv



experimental.population = the id of the experimental population 

site = site ID

start.date = day experimental population was initiated

end.date = day the experimental popuation was taken out of the field

source population = one of the four source populations used to create our experimental populations

autonomy= autonomous selfing category, high or low

size = size of experimental population, single or small

number of plants = number of plants in the experimental population 

flowers day x (1–4) = number of flowers on day 1

males day x (1-4) = number of flowers on day X

day.x.poll.date = date of day 1 or day 2 pollinator observation 

day.x.poll.time = time of day 1 or day 2 pollinator observation 

bb.day.x, mb.day.x,, sb.day.x.,  = bumblebee, medium bee, small bee visits on day 1 or day 2

total.poll.visits = The total number of pollinator visits across day 1 and day 2

visits.per.flower = the number of flowers was averaged across day 1 and day 2. The total number of visits were then divided by the average flower number.
DOI: 10.5281/zenodo.10814705
title: Support for Baker's law: facultative self-fertilization ability decreases pollen limitation in experimental colonization

description: Support for Baker’s law: facultative self-fertilization ability decreases pollen limitation in experimental colonization (datasets for paper in the American Journal of Botany)

There are 2 files associated with this manuscript. The “PLseedsetindividual.csv” file contains seed set for the pollen limitation treatments for each plant in the experimental populations. Pollinator observation data is available in the “pollinator.csv” file

Description of the data and file structure:

PLseedsetindividual.csv



source.population = one of the four source populations used to create our experimental populations

autonomy= autonomous selfing category, high or low

individual = plant id

experimental.population = the id of the experimental population 

site = site ID

size = size of experimental population, single or small

date initiated = date experimental population was put in the field

dayfromstartofexperiment = derived from date, the time from the start of the experiment that the experimental population was initiated

treatment = the pollination treatment for that flower, control or supplemented

seed number = seed set for the treated flower


Pollinator.csv



experimental.population = the id of the experimental population 

site = site ID

start.date = day experimental population was initiated

end.date = day the experimental popuation was taken out of the field

source population = one of the four source populations used to create our experimental populations

autonomy= autonomous selfing category, high or low

size = size of experimental population, single or small

number of plants = number of plants in the experimental population 

flowers day x (1–4) = number of flowers on day 1

males day x (1-4) = number of flowers on day X

day.x.poll.date = date of day 1 or day 2 pollinator observation 

day.x.poll.time = time of day 1 or day 2 pollinator observation 

bb.day.x, mb.day.x,, sb.day.x.,  = bumblebee, medium bee, small bee visits on day 1 or day 2

total.poll.visits = The total number of pollinator visits across day 1 and day 2

visits.per.flower = the number of flowers was averaged across day 1 and day 2. The total number of visits were then divided by the average flower number.
DOI: 10.5281/zenodo.10805357
title: LC-MS raw data for "A new generation of nanobody research tools using improved mass spectrometry-based discovery methods"

description: This dataset contains LC-MS raw data for VHH identifications from the project "A new generation of nanobody research tools using improved mass spectrometry-based discovery methods." The descriptions of the raw data files are in Summary_Table_MS_RawData.pdf.  Files are in the format of Thermo RAW in zip archives.
DOI: 10.5281/zenodo.10805358
title: LC-MS raw data for "A new generation of nanobody research tools using improved mass spectrometry-based discovery methods"

description: This dataset contains LC-MS raw data for VHH identifications from the project "A new generation of nanobody research tools using improved mass spectrometry-based discovery methods." The descriptions of the raw data files are in Summary_Table_MS_RawData.pdf.  Files are in the format of Thermo RAW in zip archives.
DOI: 10.5281/zenodo.10814809
title: censo-santafe-1887-bibliotecas

description: Datos sobre bibliotecas extraídos del Primer Censo de la Provincia de Santa Fe (Argentina) levantado en los días 6, 7 y 8 de junio de 1887.
DOI: 10.5281/zenodo.10814808
title: censo-santafe-1887-bibliotecas

description: Datos sobre bibliotecas extraídos del Primer Censo de la Provincia de Santa Fe (Argentina) levantado en los días 6, 7 y 8 de junio de 1887.
DOI: 10.5281/zenodo.7691770
title: The Brill Knowledge Graph:  A Database of Bibliographic References and Index Terms extracted from Books in Humanities and Social Sciences

description: We present a complete dataset of linked bibliography and index data, partially disambiguated and augmented with references to external resources, extracted from the Brill’s archive in the field of Classics. Processed book identifiers are listed in a separate text file. Text fragments extracted from different books via this process are then parsed and compared using a string-based similarity metric to form clusters of bibliographic references to the same published work or (variants of) the same subjects discussed in these books. The entire set of references was then disambiguated using Google Books and Crossref APIs.

Paper about extraction pipeline

Paper about extracted KG
DOI: 10.5281/zenodo.7691771
title: The Brill Knowledge Graph:  A Database of Bibliographic References and Index Terms extracted from Books in Humanities and Social Sciences

description: We present a complete dataset of linked bibliography and index data, partially disambiguated and augmented with references to external resources, extracted from the Brill’s archive in the field of Classics. Processed book identifiers are listed in a separate text file. Text fragments extracted from different books via this process are then parsed and compared using a string-based similarity metric to form clusters of bibliographic references to the same published work or (variants of) the same subjects discussed in these books. The entire set of references was then disambiguated using Google Books and Crossref APIs.

Paper about extraction pipeline

Paper about extracted KG
DOI: 10.6069/gx8c-w419
title: Development of a Mass Spectrometry-Based Method for Quantification of Total Cashew Protein in Roasting Oil

description: Background: Food allergen cross-contact during food preparation and production is one of the causes of unintentional allergen presence in packaged foods. However, very little is known about allergen cross-contact in shared frying or roasting oil, which prevents the establishment of effective allergen controls and may put allergic individuals at risk. To better understand the quantity of allergen transferred to frying oil and subsequent products, an analytical method that is capable of quantifying protein in oil is of utmost importance.
Objective: The goal of this study was to develop a parallel reaction monitoring LC-MS/MS method to quantify the amount of cashew protein in shared roasting oil. 
Methods: The sample preparation method was evaluated to improve protein extractability and peptide performance. Four quantitative peptides representing cashew 2S and 11S proteins were selected as targets based on their sensitivity, heat stability, and specificity. A calibration strategy was developed to quantify the amount of total cashew protein in oil. Method performance was evaluated using a heated cashew-in-oil model system.
Results: The method showed high recovery in oil samples spiked with 100 or 10 ppm total cashew protein heated at 138 or 166 °C for 2-30 minutes. Samples (100 ppm total cashew protein) heated for 30 minutes had more than 90% recovery when treated at 138 °C and more than 50% when heated at 166 °C.
Conclusion: The method is fit-for-purpose for the analysis of cashew allergen cross-contact in oil.
Highlights: A novel MS-based method was developed that can accurately quantify the amount of cashew protein present in heated oil.
DOI: 10.34740/kaggle/dsv/7853786
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.5281/zenodo.10814834
title: InPPO Lite 2024 Oktoberfest Workshop Dataset
DOI: 10.5281/zenodo.10810970
title: InPPO Lite 2024 Oktoberfest Workshop Dataset
DOI: 10.5281/zenodo.10715460
title: Annotated UI Element Dataset for Desktop Environments

description: Introducing a specialized dataset containing high-resolution screenshots from various desktop environments, focusing on annotating individual UI components. This dataset is designed to enhance the accuracy of UI element identification and classification within desktop applications, enabling the extraction of hierarchical structures.

Desktop UI Detection Dataset.zip

This resource contains a set of 100 general-purpose screenshots intended for the training set.

Test Desktop UI Detection Dataset.zip

This resource is aimed at evaluating the models trained with the previous screenshots, using a set of captures from a specific business process.

The images have been organized into six groups (G), each representing a unique set of screenshots from the same type of application. These groups are distinguished by their levels of complexity and the depth of their UI hierarchies:



G1: PDF Reader. A combination of web and native applications for interacting with PDF documents.

G2: Public Administration Courses Manager. A web-based application used in the student admission process for public learning programs (these images are not provided due to privacy issues concerning the business process).

G3: Customer Relationship Management (CRM) System. A web-based application for managing business data.

G4: Email Client. A mix of web and native applications used for managing email communications.

G5: File Explorer. A native application for navigating the file system in Windows.

G6: Learning Management System (LMS). A web-based application used to manage courses and students for a given educational institution.


Two folders are provided, each containing all the groups mentioned. These folders represent captures with the application from the corresponding group in fullscreen, or captures with the application from the corresponding group overlapping another application randomly selected from one of the remaining groups (Overlapped).
DOI: 10.5281/zenodo.10715459
title: Annotated UI Element Dataset for Desktop Environments

description: Introducing a specialized dataset containing high-resolution screenshots from various desktop environments, focusing on annotating individual UI components. This dataset is designed to enhance the accuracy of UI element identification and classification within desktop applications, enabling the extraction of hierarchical structures.

Desktop UI Detection Dataset.zip

This resource contains a set of 100 general-purpose screenshots intended for the training set.

Test Desktop UI Detection Dataset.zip

This resource is aimed at evaluating the models trained with the previous screenshots, using a set of captures from a specific business process.

The images have been organized into six groups (G), each representing a unique set of screenshots from the same type of application. These groups are distinguished by their levels of complexity and the depth of their UI hierarchies:



G1: PDF Reader. A combination of web and native applications for interacting with PDF documents.

G2: Public Administration Courses Manager. A web-based application used in the student admission process for public learning programs (these images are not provided due to privacy issues concerning the business process).

G3: Customer Relationship Management (CRM) System. A web-based application for managing business data.

G4: Email Client. A mix of web and native applications used for managing email communications.

G5: File Explorer. A native application for navigating the file system in Windows.

G6: Learning Management System (LMS). A web-based application used to manage courses and students for a given educational institution.


Two folders are provided, each containing all the groups mentioned. These folders represent captures with the application from the corresponding group in fullscreen, or captures with the application from the corresponding group overlapping another application randomly selected from one of the remaining groups (Overlapped).
DOI: 10.5281/zenodo.10815043
title: tricuspidalestes_caeruleus_spades_01.fasta.gz

description: ..
DOI: 10.5281/zenodo.10815042
title: tricuspidalestes_caeruleus_spades_01.fasta.gz

description: ..
DOI: 10.5281/zenodo.10815071
title: brycinus_macrolepidotus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.10815070
title: brycinus_macrolepidotus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.10815055
title: Mecanismos de Democracia Directa en América Latina de 2023
DOI: 10.5281/zenodo.10815054
title: Mecanismos de Democracia Directa en América Latina de 2023
DOI: 10.5281/zenodo.10815114
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Friedrich von Hoyningen-Huene, http://www.wikidata.org/entity/Q12362794. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q12362794
DOI: 10.5281/zenodo.10815113
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Friedrich von Hoyningen-Huene, http://www.wikidata.org/entity/Q12362794. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q12362794
DOI: 10.5281/zenodo.10815124
title: Indicadores electorales en América Latina de 2024
DOI: 10.5281/zenodo.10815125
title: Indicadores electorales en América Latina de 2024
DOI: 10.5281/zenodo.10814964
title: SupplementaryTable2_Rodriguez-Castro2024_BiologicalResearch

description: Supplementay Table 2 from Rodríguez-Castro et al., 2024. The long-chain flavodoxin FldX1 improves the biodegradation of 4-hydroxyphenylacetate and 3-hydroxyphenylacetate and counteracts the oxidative stress associated to aromatic catabolism in Paraburkholderia xenovorans.

Supplementary Table 2. Identification and quantification of proteins from P. xenovorans strains by mass spectrometry analysis
DOI: 10.5281/zenodo.10814965
title: SupplementaryTable2_Rodriguez-Castro2024_BiologicalResearch

description: Supplementay Table 2 from Rodríguez-Castro et al., 2024. The long-chain flavodoxin FldX1 improves the biodegradation of 4-hydroxyphenylacetate and 3-hydroxyphenylacetate and counteracts the oxidative stress associated to aromatic catabolism in Paraburkholderia xenovorans.

Supplementary Table 2. Identification and quantification of proteins from P. xenovorans strains by mass spectrometry analysis
DOI: 10.5281/zenodo.10801868
title: Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

description: This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks by Zijun Deng and Lev Sarkisov.
DOI: 10.5281/zenodo.10712731
title: Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

description: This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks by Zijun Deng and Lev Sarkisov.
DOI: 10.5281/zenodo.10712732
title: Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

description: This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks by Zijun Deng and Lev Sarkisov.
DOI: 10.5281/zenodo.10815016
title: Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks

description: This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article Engineering Machine Learning features to predict adsorption of carbon dioxide and nitrogen in metal-organic frameworks by Zijun Deng and Lev Sarkisov.
DOI: 10.5281/zenodo.10779336
title: FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 2)

description: Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the TEV protease landscape and models trained on it. All other FLIGHTED data is in the other Zenodo repository (refer to the paper for details).

The data is arranged in the following folders:



TEV_Landscape: contains the TEV landscape (in flighted_fitnesses.csv) and splits thereof in Splits/. The main files for model training are flighted_fitnesses.csv and the files labeled one_vs_rest, two_vs_rest, and three_vs_rest. The files labeled three_vs_rest_control within Splits/ and the read count CSV files refer to further information about the read count in the landscape; see the Supplement for details. The dictionary files are the original raw data prior to processing with FLIGHTED.

TEV_Models: contains models trained on the TEV landscape under the various splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them. The control_run/ refers to the run described in the supplement on just read counts.
DOI: 10.5281/zenodo.10779337
title: FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data (Part 2)

description: Data for FLIGHTED (Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data). This data contains the TEV protease landscape and models trained on it. All other FLIGHTED data is in the other Zenodo repository (refer to the paper for details).

The data is arranged in the following folders:



TEV_Landscape: contains the TEV landscape (in flighted_fitnesses.csv) and splits thereof in Splits/. The main files for model training are flighted_fitnesses.csv and the files labeled one_vs_rest, two_vs_rest, and three_vs_rest. The files labeled three_vs_rest_control within Splits/ and the read count CSV files refer to further information about the read count in the landscape; see the Supplement for details. The dictionary files are the original raw data prior to processing with FLIGHTED.

TEV_Models: contains models trained on the TEV landscape under the various splits. Each model folder contains hyperparameters, training history, and predictions on the test set which can be used to evaluate model performance. Raw model parameters are not provided for fine-tuned models due to size; contact us if you want them. The control_run/ refers to the run described in the supplement on just read counts.
DOI: 10.5281/zenodo.10815066
title: Capacidad de resiliencia de la democracia: Resiliencia electoral de 2023
DOI: 10.5281/zenodo.10815067
title: Capacidad de resiliencia de la democracia: Resiliencia electoral de 2023
DOI: 10.5281/zenodo.10815030
title: Comprehensive single-cell atlas of the mouse retina

description: Abstract:

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.

 

Data description:

1. MRCA: scRNA-seq of the mouse retina - all cellsThis file contains the full 330K single cells of the MRCA.2. MRCA: scRNA-seq of the mouse retina - bipolar cell subclassThis file contains 147K single cells for bipolar cell subcass of the MRCA.

3. MRCA: scRNA-seq of the mouse retina - RGC subclassThis file contains 77K single cells for retinal ganglion cell subclass of the MRCA.

4. MRCA: scRNA-seq of the mouse retina - amacrine cell subclassThis file contains 43K single cells for amacrine cell subclass of the MRCA.