cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5070518
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 62% to field-wide reproducibility, based on the types of files submitted to GEO. Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.
DOI: 10.5281/zenodo.5074240
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 62% to field-wide reproducibility, based on the types of files submitted to GEO. Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.
DOI: 10.5281/zenodo.5139281
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.
DOI: 10.5281/zenodo.5356064
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - This release includes GEO series up to Dec-31, 2020; - Fixed xlrd missing optional dependency, which affected import of some xls files, previously we were using only openpyxl (thanks to anonymous reviewer); - All files in supplementary _RAW.tar files were checked for p values, previously _RAW.tar files were completely omitted, alas (thanks to anonymous reviewer). Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.
DOI: 10.5281/zenodo.5702316
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - Fixed some missing RAW files, that had failed to download. - This release includes GEO series up to Dec-31, 2020; - Fixed xlrd missing optional dependency, which affected import of some xls files, previously we were using only openpyxl (thanks to anonymous reviewer); - All files in supplementary _RAW.tar files were checked for p values, previously _RAW.tar files were completely omitted, alas (thanks to anonymous reviewer). Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row. Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.
DOI: 10.5281/zenodo.6769241
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - This release includes GEO series up to Dec-31, 2020; Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row. Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.
DOI: 10.5281/zenodo.6795313
title: Field-wide assessment of differential HT-seq from NCBI GEO database

description: We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - This release includes GEO series published up to Dec-31, 2020; Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series. - output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. - output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. - output/publications.csv, publication info of NCBI GEO series. - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/spots.csv, NCBI SRA sequencing run metadata. - output/cancer.csv, cancer related experiment accessions. - output/transcription_factor.csv, TF related experiment accessions. - output/single-cell.csv, single cell experiment accessions. - blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import. Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.
DOI: 10.15454/x2mowo/94kbvv
title: Hierarchy_Biomass_State.tab

description: :unav
DOI: 10.15454/x2mowo/15zi9w
title: Symbolic_Concept_Sample_Status.tab

description: :unav
DOI: 10.15454/x2mowo/njsnhn
title: Hierarchy_Filter_State.tab

description: :unav
DOI: 10.15454/x2mowo/avjglq
title: Hierarchy_Material.tab

description: :unav
DOI: 10.15454/x2mowo/aa1nzd
title: Symbolic_Concept_Product.csv

description: :unav
DOI: 10.15454/x2mowo/neslp2
title: Hierarchy_Treatment.tab

description: :unav
DOI: 10.15454/x2mowo/tpw3le
title: Hierarchy_Particle_separation.csv

description: :unav
DOI: 10.15454/x2mowo/31eapf
title: Symbolic_Concept_Particle_separation.csv

description: :unav
DOI: 10.15454/x2mowo/ed0afy
title: Hierarchy_Product_Form.tab

description: :unav
DOI: 10.15454/x2mowo/aojglh
title: Symbolic_Concept_calculation_method.tab

description: :unav
DOI: 10.15454/x2mowo/bpoiw8
title: Hierarchy_Decolmatage_State.tab

description: :unav
DOI: 10.15454/x2mowo/rhbfzk
title: Hierarchy_Constituent_State.tab

description: :unav
DOI: 10.15454/x2mowo/loopid
title: Hierarchy_Sample_Status.tab

description: :unav
DOI: 10.15454/x2mowo/wehn2x
title: Symbolic_Concept_Decolmatage_State.tab

description: :unav
DOI: 10.15454/x2mowo/gpoq5l
title: Symbolic_Concept_Date.tab

description: :unav
DOI: 10.15454/x2mowo/amxdl7
title: Hierarchy_Quantity.tab

description: :unav
DOI: 10.15454/x2mowo/olv3ta
title: Symbolic_Concept_Reference_Liquid.tab

description: :unav
DOI: 10.15454/x2mowo/qo4ly5
title: Symbolic_Concept_Biomass_Fraction.tab

description: :unav
DOI: 10.15454/x2mowo/nsyynu
title: Symbolic_Concept_Treatment.tab

description: :unav
DOI: 10.15454/x2mowo/pcfewa
title: Hierarchy_Constituent.tab

description: :unav
DOI: 10.15454/x2mowo/nedeqk
title: Symbolic_Concept_Product_Form.tab

description: :unav
DOI: 10.15454/x2mowo/catx4w
title: Hierarchy_Product.csv

description: :unav
DOI: 10.15454/x2mowo/twsriz
title: Relation_Concept.csv

description: :unav
DOI: 10.15454/x2mowo/yauagf
title: Symbolic_Concept_Material.tab

description: :unav
DOI: 10.15454/x2mowo/1tafnl
title: Quantity_Concept.csv

description: :unav
DOI: 10.15454/x2mowo/dykaiu
title: Hierarchy_Relation.tab

description: :unav
DOI: 10.15454/x2mowo/kejlgb
title: Hierarchy_Turbo_separator.tab

description: :unav
DOI: 10.15454/x2mowo/ydxttl
title: Hierarchy_Biomass_Fraction.tab

description: :unav
DOI: 10.15454/x2mowo/826fi2
title: Hierarchy_calculation_method.tab

description: :unav
DOI: 10.15454/x2mowo/fjqghe
title: Symbolic_Concept_Filter_State.tab

description: :unav
DOI: 10.15454/x2mowo/f0niby
title: Symbolic_Concept_Turbo_separator.tab

description: :unav
DOI: 10.15454/x2mowo/5x0lsy
title: Symbolic_Concept_Constituent_State.tab

description: :unav
DOI: 10.15454/x2mowo/6in1yh
title: Symbolic_Concept_Biomass_State.tab

description: :unav
DOI: 10.15454/x2mowo/s02gte
title: Hierarchy_Reference_Liquid.tab

description: :unav
DOI: 10.15454/x2mowo/eijv7i
title: Symbolic_Concept_Constituent.tab

description: :unav
DOI: 10.15454/x2mowo/nwacdv
title: Hierarchy_Product-1.csv
DOI: 10.15454/x2mowo/orsdoa
title: BIOREFINERY.owl

description: :unav
DOI: 10.15454/x2mowo/dwt4y6
title: Symbolic_Concept_Product-1.csv
DOI: 10.15454/x2mowo/afomxp
title: Relation_Concept-1.csv
DOI: 10.15454/x2mowo/hxd5ht
title: Quantity_Concept-1.tab
DOI: 10.15454/x2mowo/j1qbaq
title: BIOREFINERY-1.owl
DOI: 10.57745/4akcsh
title: BIOREFINERY-1.owl

description: :unav
DOI: 10.18112/openneuro.ds003216.v3.0.8
title: Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset
DOI: 10.6084/m9.figshare.21890598
title: BAM alignments and BAI indexes

description: BAM output of the bioinformatic analyses for the project
DOI: 10.35078/qmvx0w
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Berilo

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: 10.6084/m9.figshare.11663481
title: Prediction of Culture Based on Automated Detection of Multimodal Social Signals

description: Users require conversational virtual characters to be socially and culturally aware in order to develop trust and relationship(s). This is dependent on the goal and setting of the interaction. In previous research It has been suggested that cultures that require high context (increased use of nonverbal signals) and low context (more direct verbal communication) during communication differ in their use of nonverbal displays. The aim of this paper is to investigate the following: 1) whether group membership of high or low context cultures can be predicted based on detection of nonverbal signals in the impression formation phase of a dialogue; and 2) whether multimodal approaches would lead to better and improved accuracy compared to unimodal approaches. As part of this study and to collect data, nonverbal signals were captured during two media skills training workshops. Analysis of 35 media interviews revealed that a multimodal approach produces a higher F measure at predicting high and low context cultures (0.772 to 0.848 vs 0.733 to 0.771 for unimodal channels). These findings contribute to the development of conversational virtual characters that demonstrate more culturally aware communication styles in the initial stages of an interaction with their users.
DOI: 10.6084/m9.figshare.11663481.v1
title: Prediction of Culture Based on Automated Detection of Multimodal Social Signals

description: Users require conversational virtual characters to be socially and culturally aware in order to develop trust and relationship(s). This is dependent on the goal and setting of the interaction. In previous research It has been suggested that cultures that require high context (increased use of nonverbal signals) and low context (more direct verbal communication) during communication differ in their use of nonverbal displays. The aim of this paper is to investigate the following: 1) whether group membership of high or low context cultures can be predicted based on detection of nonverbal signals in the impression formation phase of a dialogue; and 2) whether multimodal approaches would lead to better and improved accuracy compared to unimodal approaches. As part of this study and to collect data, nonverbal signals were captured during two media skills training workshops. Analysis of 35 media interviews revealed that a multimodal approach produces a higher F measure at predicting high and low context cultures (0.772 to 0.848 vs 0.733 to 0.771 for unimodal channels). These findings contribute to the development of conversational virtual characters that demonstrate more culturally aware communication styles in the initial stages of an interaction with their users.
DOI: 10.6084/m9.figshare.11663481.v2
title: Prediction of Culture Based on Automated Detection of Multimodal Social Signals

description: Users require conversational virtual characters to be socially and culturally aware in order to develop trust and relationship(s). This is dependent on the goal and setting of the interaction. In previous research It has been suggested that cultures that require high context (increased use of nonverbal signals) and low context (more direct verbal communication) during communication differ in their use of nonverbal displays. The aim of this paper is to investigate the following: 1) whether group membership of high or low context cultures can be predicted based on detection of nonverbal signals in the impression formation phase of a dialogue; and 2) whether multimodal approaches would lead to better and improved accuracy compared to unimodal approaches. As part of this study and to collect data, nonverbal signals were captured during two media skills training workshops. Analysis of 35 media interviews revealed that a multimodal approach produces a higher F measure at predicting high and low context cultures (0.772 to 0.848 vs 0.733 to 0.771 for unimodal channels). These findings contribute to the development of conversational virtual characters that demonstrate more culturally aware communication styles in the initial stages of an interaction with their users.
DOI: 10.5281/zenodo.7530009
title: IDSL.IPA peak list for a test file

description: IDSL.IPA peak list for a test file pool_P1_A1-A12.mzML from the ftp://massive.ucsd.edu/MSV000088661/ ( IROA metabolite standards )
DOI: 10.5281/zenodo.7530008
title: IDSL.IPA peak list for a test file

description: IDSL.IPA peak list for a test file pool_P1_A1-A12.mzML from the ftp://massive.ucsd.edu/MSV000088661/ ( IROA metabolite standards )
DOI: 10.6084/m9.figshare.21890694
title: Prediction of Culture Based on Automated Detection of Multimodal Social Signals

description: Embodied conversational agents are required to be socially and culturally aware to bring about trust and form a<br> relationship with users. This is fundamentally important during the first few seconds of the user-agent interaction.<br> Previous research has not accounted for culture in communication style but has rather focused on the look of the<br> agent. Similarly, research has predominately focused on unimodal or bimodal channels of communication which<br> render the human-agent interaction unnatural. The first aim of this study is to investigate whether group<br> membership of high or low context cultures can be predicted based on detection of nonverbal signals in the<br> impression formation phase of a dialogue. The second aim is to investigate whether multimodal approaches would<br> lead to better and improved accuracy compared to unimodal approaches which could lead to a more natural<br> interaction. The third aim is to identify the best predictor for media skills training communication context. To do<br> this, nonverbal signals were captured during media interview training workshops. Analysis of 32 on-camera media<br> interviews revealed that a multimodal approach produces a higher accuracy at predicting high and low context<br> cultures. The findings suggest that multimodal channels of communication produce better accuracy than that of<br> the unimodal channels. The Decision Trees Classifier performed well for multimodal communication analysis.<br> These findings could contribute to the development of more natural embodied conversational agents that consider<br> different cultural communication styles.
DOI: 10.6084/m9.figshare.21890694.v1
title: Prediction of Culture Based on Automated Detection of Multimodal Social Signals

description: Embodied conversational agents are required to be socially and culturally aware to bring about trust and form a<br> relationship with users. This is fundamentally important during the first few seconds of the user-agent interaction.<br> Previous research has not accounted for culture in communication style but has rather focused on the look of the<br> agent. Similarly, research has predominately focused on unimodal or bimodal channels of communication which<br> render the human-agent interaction unnatural. The first aim of this study is to investigate whether group<br> membership of high or low context cultures can be predicted based on detection of nonverbal signals in the<br> impression formation phase of a dialogue. The second aim is to investigate whether multimodal approaches would<br> lead to better and improved accuracy compared to unimodal approaches which could lead to a more natural<br> interaction. The third aim is to identify the best predictor for media skills training communication context. To do<br> this, nonverbal signals were captured during media interview training workshops. Analysis of 32 on-camera media<br> interviews revealed that a multimodal approach produces a higher accuracy at predicting high and low context<br> cultures. The findings suggest that multimodal channels of communication produce better accuracy than that of<br> the unimodal channels. The Decision Trees Classifier performed well for multimodal communication analysis.<br> These findings could contribute to the development of more natural embodied conversational agents that consider<br> different cultural communication styles.
DOI: 10.5281/zenodo.7529763
title: Land Cover Fraction Mapping with FORCE - Supplemental Data

description: This upload contains data required to replicate a tutorial that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the Framework for Operational Radiometric Correction for Environmental monitoring. This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files. Please refer to the above mentioned tutorial for more information.
DOI: 10.5281/zenodo.7529762
title: Land Cover Fraction Mapping with FORCE - Supplemental Data

description: This upload contains data required to replicate a tutorial that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the Framework for Operational Radiometric Correction for Environmental monitoring. This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files. Please refer to the above mentioned tutorial for more information.
DOI: 10.5281/zenodo.7530028
title: IDSL.CSA grouping of individual peaks and adduct annotation

description: IDSL.CSA grouping and adduction annotation of individual peaks for test file a test file pool_P1_A1-A12.mzML from the ftp://massive.ucsd.edu/MSV000088661/ ( IROA metabolite standards )
DOI: 10.5281/zenodo.7530027
title: IDSL.CSA grouping of individual peaks and adduct annotation

description: IDSL.CSA grouping and adduction annotation of individual peaks for test file a test file pool_P1_A1-A12.mzML from the ftp://massive.ucsd.edu/MSV000088661/ ( IROA metabolite standards )