cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21917124
title: gnomAD_v3.1.2_INS_Var

description: A breakdown of variants studied near the INS gene for allele frequencies in different populations.
DOI: 10.5281/zenodo.5710562
title: #PraCegoVer dataset

description: Automatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce. PraCegoVer arose on the Internet, stimulating users from social media to publish images, tag #PraCegoVer and add a short description of their content. Inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images. #PraCegoVer has 533,523 pairs with images and captions described in Portuguese collected from more than 14 thousand different profiles. Also, the average caption length in #PraCegoVer is 39.3 words and the standard deviation is 29.7. <strong>Dataset Structure</strong> #PraCegoVer dataset is composed of the main file <em>dataset.json</em> and a collection of compressed files named <em>images.tar.gz.partX</em><br> containing the images. The file <em>dataset.json </em>comprehends a list of json objects with the attributes: <em>user: </em>anonymized user that made the post; <em>filename: </em>image file name; <em>raw_caption: </em>raw caption; <em>caption: </em>clean caption; <em>date: </em>post date. Each instance in dataset.json is associated with exactly one image in the <em>images</em> directory whose filename is pointed by the attribute <em>filename</em>. Also, we provide a sample with five instances, so the users can download the sample to get an overview of the dataset before downloading it completely. <strong>Download Instructions</strong> If you just want to have an overview of the dataset structure, you can download <em>sample.tar.gz.</em> But, if you want to use the dataset, or any of its subsets (63k and 173k), you must download all the files and run the following commands to uncompress and join the files: <pre><code class="language-bash">cat images.tar.gz.part* &gt; images.tar.gz tar -xzvf images.tar.gz</code></pre> Alternatively, you can download the entire dataset from the terminal using the python script download_dataset.py available in PraCegoVer repository. In this case, first, you have to download the script and create an access token here. Then, you can run the following command to download and uncompress the image files: <pre><code class="language-bash">python download_dataset.py --access_token=&lt;your access token&gt;</code></pre>
description: Funding acknowledgements: G.O.S. is funded by the São Paulo Research Foundation (FAPESP) (2019/24041-4). E.L.C. and S.A. are partially funded by H.IAAC (Artificial Intelligence and Cognitive Architectures Hub). S.A. is also partially funded by FAPESP (2013/08293-7), a CNPq PQ-2 grant (315231/2020-3), and Google LARA 2020. The opinions expressed in this work do not necessarily reflect those of the funding agencies.
DOI: 10.3886/9tx6-0763
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Baseline Data Set
DOI: 10.6084/m9.figshare.21867942.v4
title: ASD

description: Research conducted with parents of children or adolescents with ASD of both sexes.
DOI: 10.35078/sywsfs
title: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Diamantina

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.3886/s1p2-1k68
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Baseline Data Set
DOI: 10.3886/f9ab-ve30
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Baseline Data Set
DOI: 10.3886/q88t-px30
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Final Data Set
DOI: 10.3886/dg7m-ne12
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Final Data Set
DOI: 10.3886/k4j3-b346
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Interim Data Set
DOI: 10.3886/231y-zd92
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Interim Data Set
DOI: 10.3886/2wnb-rf04
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Interim Analysis Report
DOI: 10.3886/c7hk-yn82
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Baseline Analysis Report
DOI: 10.3886/7d3p-nq45
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Design Report
DOI: 10.3886/74aw-xp12
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Final Analysis Report
DOI: 10.6084/m9.figshare.21867942.v5
title: ASD

description: Research conducted with parents of children or adolescents with ASD of both sexes.
DOI: 10.3886/ghbx-vs04
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Interim Analysis Report
DOI: 10.3886/3mf8-0y53
title: Philippines Compact - KALAHI-CIDSS Community Development Grants: Interim Analysis Report
DOI: 10.3886/tt67-aj96
title: Tanzania Threshold - Governance and Anticorruption: Final Analysis Report
DOI: 10.3886/yntf-jw69
title: Tanzania Threshold - Governance and Anticorruption: Final Analysis Report
DOI: 10.5066/p9uidwvv
title: Human Factors of Water Availability in the Delaware River Basin

description: Human factors that influence water availability in the Basin were discovered by reviewing hundreds of published literature items and articles from the literature following an extensive keyword search. The different factors were drawn from reviewing the literature, and datasets to support the factor were researched across open data catalogs and the world wide web. Data related to the Human Factors project water availability sectors of agriculture, industrial, municipal, and those related to ecosystem services, tourism, or other uses can be found here.
DOI: 10.3886/6b53-2886
title: Niger Threshold - IMAGINE: Final Analysis Report
DOI: 10.3886/90wc-5w29
title: Tanzania Threshold - Governance and Anticorruption: Design Report
DOI: 10.3886/y8nw-zz65
title: Tanzania Threshold - Governance and Anticorruption: Design Report
DOI: 10.6084/m9.figshare.21917970
title: Pjap_split.noendlines.FINAL.fasta.gz

description: Propylea japonica genome assembly after splitting and rescaffolding with Hi-C.
DOI: 10.6084/m9.figshare.21917970.v1
title: Pjap_split.noendlines.FINAL.fasta.gz

description: Propylea japonica genome assembly after splitting and rescaffolding with Hi-C.
DOI: 10.7910/dvn/7fq2bw
title: Cooperative Election Study (CES) Common Content, 2021

description: CES Common Content, 2021 (n = 25,700). In odd years, the CES runs a common content only post-election wave, and multiple modules. This dataset releases the common content.
DOI: 10.6084/m9.figshare.21915810.v1
title: Albanian data

description: 600 dpi figures, hi-resolution orthophoto and geometrically correct analyses data
DOI: 10.35078/nduhyf
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Pirapora | Município: Lassance

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7548606
title: California ISRM

description: This dataset contains the California-specific InMAP Source-Receptor Matrix (ISRM). The files included are listed below: <strong>ca_isrm.ncf</strong>: a NetCDF file containing the ISRM data. The relevant layers for performing ISRM calculations are PrimaryPM25, pNH4, pNO3, pSO4, and SOA. These variables have dimensions of (21705, 21705, 3) representing the 21,705 grid cells and the three elevation heights. Additional variables contain information about InMAP parameters (e.g., Temperature, WindSpeed). <strong>InMAP_gridCells.shp</strong>: a shapefile containing the geographic information associated with the ISRM file. The grid cells are indexed with a matching order to the ISRM NetCDF index. The other files (InMAP_gridCells.*) are required for opening and manipulating the shapefile. The ISRM was built from the US InMAP (link). The three elevations of release height within the ISRM are: (1) less than 57 meters, (2) between 57 and 140 meters, and (3) greater than 760 meters. This ISRM was used in Apte, J. S., Chambliss, S. E., Tessum, C. W., Marshall, J.D. (2019). A Method to Prioritize Sources for Reducing High PM<sub>2.5</sub> Exposures in Environmental Justice Communities in California. Sacramento, CA: California Air Resources Board and California Environmental Protection Agency. Available here. To learn how to run ISRM calculations, click here.
DOI: 10.5281/zenodo.7548607
title: California ISRM

description: This dataset contains the California-specific InMAP Source-Receptor Matrix (ISRM). The files included are listed below: <strong>ca_isrm.ncf</strong>: a NetCDF file containing the ISRM data. The relevant layers for performing ISRM calculations are PrimaryPM25, pNH4, pNO3, pSO4, and SOA. These variables have dimensions of (21705, 21705, 3) representing the 21,705 grid cells and the three elevation heights. Additional variables contain information about InMAP parameters (e.g., Temperature, WindSpeed). <strong>InMAP_gridCells.shp</strong>: a shapefile containing the geographic information associated with the ISRM file. The grid cells are indexed with a matching order to the ISRM NetCDF index. The other files (InMAP_gridCells.*) are required for opening and manipulating the shapefile. The ISRM was built from the US InMAP (link). The three elevations of release height within the ISRM are: (1) less than 57 meters, (2) between 57 and 140 meters, and (3) greater than 760 meters. This ISRM was used in Apte, J. S., Chambliss, S. E., Tessum, C. W., Marshall, J.D. (2019). A Method to Prioritize Sources for Reducing High PM<sub>2.5</sub> Exposures in Environmental Justice Communities in California. Sacramento, CA: California Air Resources Board and California Environmental Protection Agency. Available here. To learn how to run ISRM calculations, click here.
DOI: 10.6084/m9.figshare.21648370
title: A Flexible Zero-Inflated Poisson-Gamma Model with Application to Microbiome Sequence Count Data

description: In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) model framework to address these aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide a flexible ZIPG model framework by connecting both the mean abundance and the variability of abundances to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and mean abundance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21688991
title: Crime in Philadelphia: Bayesian Clustering with Particle Optimization

description: Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled “sharing of information” between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21918807
title: Propylea japonica annotation

description: Propylea japonica maker anotation
DOI: 10.6084/m9.figshare.21918807.v1
title: Propylea japonica annotation

description: Propylea japonica maker anotation
DOI: 10.6084/m9.figshare.19388498
title: 2021 Ocean Temperature and Salinity Observations

description: Temperature and salinity profiles for SeaGlider 670 deployed off the eastern side of Eleuthera island, The Bahamas (see glider track) during the 2021 Atlantic hurricane season.
DOI: 10.6084/m9.figshare.19388498.v2
title: 2021 Ocean Temperature and Salinity Observations

description: Temperature and salinity profiles for SeaGlider 670 deployed off the eastern side of Eleuthera island, The Bahamas (see glider track) during the 2021 Atlantic hurricane season.
DOI: 10.35078/gg3ezr
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Montes Claros | Município: Rio Pardo de Minas

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.25345/c5q815312
title: MassIVE MSV000092327 - Proteome dataset of the fission yeast during glucose starvation
title: Proteome data obtained with timsTOF Pro of the fission yeast cells exposed to glucose starvation at four time points 0 (glucose rich conditions), 15, 60 and 120 minutes
DOI: 10.6084/m9.figshare.23613861
title: Streptococcus intermedius

description: This dataset contains GWAS summary file generated from sputum metagenomic data for COPD patients
DOI: 10.6084/m9.figshare.23613861.v1
title: Streptococcus intermedius

description: This dataset contains GWAS summary file generated from sputum metagenomic data for COPD patients
DOI: 10.5281/zenodo.8025053
title: Natural Language Inference Dataset for Software Engineering

description: Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.
DOI: 10.5281/zenodo.8028035
title: Natural Language Inference Dataset for Software Engineering

description: Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.
DOI: 10.5281/zenodo.8028577
title: Natural Language Inference Dataset for Software Engineering

description: Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.
DOI: 10.5281/zenodo.8051063
title: Natural Language Inference Dataset for Software Engineering

description: Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.
DOI: 10.5281/zenodo.8025052
title: Natural Language Inference Dataset for Software Engineering

description: Active research in requirements engineering and software engineering necessitates the application of Natural Language Processing (NLP) techniques to address unique challenges and enhance software quality. However, there is a dearth of effective Natural Language Inference (NLI) datasets for training neural network models to generate distributed sentence representations and tackle diverse NLP tasks. In this paper, we present a NLI dataset, tailored specifically to software engineering, empowers neural network models to effectively handle NLP tasks in this domain. The creation of this dataset involved meticulous annotation and careful consideration of diverse sources, including software documentation, user guides, App reviews and different articles related to software systems. Our dataset maintains compatibility with existing NLI datasets like Stanford Natural Language Inference, facilitating seamless adaptation of models without additional preprocessing.