cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9917162.v1
title: Crowdsourced dataset to study the generation and impact of text highlighting in classification tasks

description: Here we present the datasets derived from our experiments on using crowdsourcing for document classification tasks. These experiments resemble a two-step process that first highlights excerpts from the text and then leverage these to workers for classification. Thus our experiments groups into highlighting generation and classification. For generating highlights, we leverage crowdsourcing and automatic approaches such us extractive summarization and question answering models. For our classification experiments, we consider documents from two different domains: systematic literature reviews and amazon product reviews. Specifically, we study how highlighting text passages could aid workers in judging the relevance of a document given an input question. We spec these datasets to benefit not only to study these particular problem domains but a broader set of classification problems where individual judgments from workers are scarce.<br>
DOI: 10.6084/m9.figshare.9917111.v1
title: Abundance data

description: Raw data for Progress in Oceanography submission. Grigor et al. "Non-carnivorous feeding in Arctic chaetognaths".Chaetognath abundance data.
DOI: 10.6084/m9.figshare.9917162.v2
title: Crowdsourced datasets to study the generation and impact of text highlighting in classification tasks

description: Here we present the datasets derived from our experiments on using crowdsourcing for document classification tasks. These experiments resemble a two-step process that first highlights excerpts from the text and then leverage these to workers for classification. Thus our experiments groups into highlighting generation and classification. For generating highlights, we leverage crowdsourcing and automatic approaches such us extractive summarization and question answering models. For our classification experiments, we consider documents from two different domains: systematic literature reviews and amazon product reviews. Specifically, we study how highlighting text passages could aid workers in judging the relevance of a document given an input question. We spec these datasets to benefit not only to study these particular problem domains but a broader set of classification problems where individual judgments from workers are scarce.<br><br>In a nutshell, the datasets represent two kinds of tasks:<br>- classification tasks with highlighting support.- highlighting tasks, where the workers highlight evidence.<br><b>Classification tasks</b><br>In this task, workers classified documents based on a given predicate. <br><b>classification tasks using crowdsourced highlights</b><br>Files:<i>- classification_amazon-crowd-highlights.csv</i><i>- classification_oa-crowd-highlights.csv</i><i>- classification_tech-crowd-highlights.csv</i><i>- classification_tech-3x12-crowd-highlights.csv</i><i>- classification_tech-6x6-crowd-highlights.csv</i><br><b>classification tasks using ML-generated highlights</b><br>Files:<i>- classification_amazon-ML-highlights.csv</i><i>- classification_oa-ML-highlights.csv</i><i>- classification_tech-ML-highlights.csv</i><br><br><b>Highlighting tasks</b><br><b>crowdsourced highlights</b><br><br>In this task, workers highlighted excerpts from documents that are relevant to a given predicate, to support future classification tasks.<br>File: <i>crowdsourced_highlights.csv.</i><br><br><b>ML-generated highlights</b><br>We also consider automatic approaches to generate text highlights — specifically, extractive summarization and question-answering models.<br><br>File: <i>ml_highlights.csv.</i>
DOI: 10.6084/m9.figshare.9917072
title: Spatially dependent flood probabilities to support the design of civil infrastructure systems - Data sets

description: This is the data set for the paper "Spatially dependent flood probabilities to support the design of civil infrastructure systems". The data set contains seven files for seven rain stations in the case study. Each file contains the name of station, lon, lat, elevation, year, and extreme rainfall for 1h, 2h, 3h, 6h, 9h, 12h, and 36h.
DOI: 10.6084/m9.figshare.9917087
title: CavBench datasets

description: CavBench's protein cavity datasets, which include cavities output by the following cavity detection methods: Fpocket, KVFinder, GHECOM, and Gaussian Finder. It is clear that ground truth dataset of protein cavities is also included.
DOI: 10.6084/m9.figshare.9917162.v3
title: Crowdsourced datasets to study the generation and impact of text highlighting in classification tasks

description: Here we present the datasets derived from our experiments on using crowdsourcing for document classification tasks. These experiments resemble a two-step process that first highlights excerpts from the text and then leverage these to workers for classification. Thus our experiments groups into highlighting generation and classification. For generating highlights, we leverage crowdsourcing and automatic approaches such us extractive summarization and question answering models. For our classification experiments, we consider documents from two different domains: systematic literature reviews and amazon product reviews. Specifically, we study how highlighting text passages could aid workers in judging the relevance of a document given an input question. We spec these datasets to benefit not only to study these particular problem domains but a broader set of classification problems where individual judgments from workers are scarce.<br><br>In a nutshell, the datasets represent two kinds of tasks:<br>- classification tasks with highlighting support.- highlighting tasks, where the workers highlight evidence.<br><b>Classification tasks</b><br>In this task, workers classified documents based on a given predicate. <br><b>classification tasks using crowdsourced highlights</b><br>Files:<i>- classification_amazon-crowd-highlights.csv</i><i>- classification_oa-crowd-highlights.csv</i><i>- classification_tech-crowd-highlights.csv</i><i>- classification_tech-3x12-crowd-highlights.csv</i><i>- classification_tech-6x6-crowd-highlights.csv</i><br><b>classification tasks using ML-generated highlights</b><br>Files:<i>- classification_amazon-ML-highlights.csv</i><i>- classification_oa-ML-highlights.csv</i><i>- classification_tech-ML-highlights.csv</i><br><br><b>Highlighting tasks</b><br><b>crowdsourced highlights</b><br><br>In this task, workers highlighted excerpts from documents that are relevant to a given predicate, to support future classification tasks.<br>File: <i>crowdsourced_highlights.csv.</i><br><i><br></i>The file contains one line per highlight (generated by one worker); the column that holds the highlighted fragment(s) is <i>highlighted_text</i>. The <i>highlighted_text</i> is a "list of lists" (Python syntax), so iterating over this list will give you the text fragment generated by one worker. Also, the <i>experiment</i> column indicates domain + task design. So, to get the highlights used in the classification experiments, use the rows that end with <i>"-highlight"</i>.<br><br><b>ML-generated highlights</b><br>We also consider automatic approaches to generate text highlights — specifically, extractive summarization and question-answering models.<br><br>File: <i>ml_highlights.csv.</i>
DOI: 10.6084/m9.figshare.9917081
title: MRT_classes.csv

description: <b>Data from:</b><b>Troia, M.J. & McManamay, R.A. (in press) Biogeographic classification of streams using fish community- and trait-environment relationships. <i>Diversity and Distributions</i>, DOI: TBD</b><br>The data include a biogeographic classification of 852,543 stream reaches for the eastern United States, specifically regions 1, 2, 3, 4, 5 and 6 of the National Hydrography Dataset (NHD) version 1.Column 1 (COMID) contains the NHD COMIDs, Column 2 (HUC2) contains the NHD region, Column 3 (class_tax) contains the taxonomy-based class assignment, and Column 4 (class_trait) contains the trait-based class assignment. Both class assignments correspond to those in Figure 5 of the article.<br> If data are used, please use the following citation:<b>Troia, M.J. & McManamay, R.A. (in press) Biogeographic classification of streams using fish community- and trait-environment relationships. <i>Diversity and Distributions</i>, DOI: TBD</b><br>Please refer to the article for methods and background.<br><br>
DOI: 10.6084/m9.figshare.9917081.v1
title: MRT_classes.csv

description: <b>Data from:</b><b>Troia, M.J. & McManamay, R.A. (in press) Biogeographic classification of streams using fish community- and trait-environment relationships. <i>Diversity and Distributions</i>, DOI: TBD</b><br>The data include a biogeographic classification of 852,543 stream reaches for the eastern United States, specifically regions 1, 2, 3, 4, 5 and 6 of the National Hydrography Dataset (NHD) version 1.Column 1 (COMID) contains the NHD COMIDs, Column 2 (HUC2) contains the NHD region, Column 3 (class_tax) contains the taxonomy-based class assignment, and Column 4 (class_trait) contains the trait-based class assignment. Both class assignments correspond to those in Figure 5 of the article.<br> If data are used, please use the following citation:<b>Troia, M.J. & McManamay, R.A. (in press) Biogeographic classification of streams using fish community- and trait-environment relationships. <i>Diversity and Distributions</i>, DOI: TBD</b><br>Please refer to the article for methods and background.<br><br>
DOI: 10.6084/m9.figshare.9917030
title: Ground truth cavity dataset in XML

description: Ground truth cavity dataset in XML.
DOI: 10.6084/m9.figshare.9917021
title: Ground truth cavity dataset in CVS

description: Ground truth cavity dataset in CVS.<br><br>
DOI: 10.6084/m9.figshare.9917018
title: Sexual_addiction_med_students_LATAM.xlsx

description: The data was collected as part of the research study, frequency and associated factors related to sexual addiction in medical students from sixteen Latin American cities in 2016: First regional experience. <br><br>
DOI: 10.6084/m9.figshare.9917030.v1
title: Ground truth cavity dataset in XML

description: Ground truth cavity dataset in XML.
DOI: 10.6084/m9.figshare.9917021.v1
title: Ground truth cavity dataset in CVS

description: Ground truth cavity dataset in CVS.<br><br>
DOI: 10.6084/m9.figshare.9917015
title: KVFinder's cavity dataset in XML

description: KVFinder's cavity dataset in XML.<br>This dataset describes the protein cavities output by a protein cavity detection method called KVFinder. This method is described in the article available at:https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-197
DOI: 10.6084/m9.figshare.9917009
title: GHECOM's cavity dataset in XML

description: GHECOM's cavity dataset in XML.<br>This dataset describes the protein cavities output by a protein cavity detection method called GHECOM. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pubmed/19938154
DOI: 10.6084/m9.figshare.9917012
title: KVFinder's cavity dataset in CSV

description: KVFinder's cavity dataset in CVS.<br>This dataset describes the protein cavities output by a protein cavity detection method called KVFinder. This method is described in the article available at:https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-197<br>
DOI: 10.6084/m9.figshare.9917018.v2
title: Sexual_addiction_med_students_LATAM.xlsx

description: The data was collected as part of the research study, frequency and associated factors related to sexual addiction in medical students from sixteen Latin American cities in 2016: First regional experience. <br><br>
DOI: 10.6084/m9.figshare.9917015.v1
title: KVFinder's cavity dataset in XML

description: KVFinder's cavity dataset in XML.<br>This dataset describes the protein cavities output by a protein cavity detection method called KVFinder. This method is described in the article available at:https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-197
DOI: 10.6084/m9.figshare.9917018.v1
title: Sexual_addiction_med_students_LATAM.xlsx

description: The data was collected as part of the research study, frequency and associated factors related to sexual addiction in medical students from sixteen Latin American cities in 2016: First regional experience. <br><br>
DOI: 10.6084/m9.figshare.9917009.v1
title: GHECOM's cavity dataset in XML

description: GHECOM's cavity dataset in XML.<br>This dataset describes the protein cavities output by a protein cavity detection method called GHECOM. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pubmed/19938154
DOI: 10.6084/m9.figshare.9916766
title: Australian Science Communicators survey 2019

description: This survey records the remuneration, qualifications and satisfaction levels of self-identified Australian science communicators in 2019.
DOI: 10.6084/m9.figshare.9916751
title: GHECOM's cavity dataset in CSV

description: GHECOM's cavity dataset in CSV.<br>This dataset describes the protein cavities output by a protein cavity detection method called GHECOM. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pubmed/19938154<br><br>
DOI: 10.6084/m9.figshare.9916745
title: Gaussian Finder's cavity dataset in CSV

description: Gaussian Finder's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916733
title: Gaussian Finder's cavity dataset in XML

description: Gaussian Finder's cavity dataset in XML. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916766.v2
title: Australian Science Communicators survey 2019

description: This survey records the remuneration, qualifications and satisfaction levels of self-identified Australian science communicators in 2019.
DOI: 10.6084/m9.figshare.9916766.v1
title: Australian Science Communicators survey 2019

description: This survey records the remuneration, qualifications and satisfaction levels of self-identified Australian science communicators in 2019.
DOI: 10.6084/m9.figshare.9916730
title: Coordinate Shifts from Molecular architecture of the SYCP3 fibre and its interaction with DNA

description: The synaptonemal complex (SC) keeps homologous chromosomes in close alignment during meiotic recombination. A hallmark of the SC is the presence of its constituent protein SYCP3 on the chromosome axis. During SC assembly, SYCP3 is deposited on both axes of the homologue pair, forming axial elements that fuse into the lateral element (LE) in the tripartite structure of the mature SC. We have used cryo-electron tomography and atomic force microscopy to study the mechanism of assembly and DNA binding of the SYCP3 fibre. We find that the three-dimensional architecture of the fibre is built on a highly irregular arrangement of SYCP3 molecules displaying very limited local geometry. Interaction between SYCP3 molecules is driven by the intrinsically disordered tails of the protein, with no contact between the helical cores, resulting in a flexible fibre assembly. We demonstrate that the SYCP3 fibre can engage in extensive interactions with DNA, indicative of an efficient mechanism for incorporation of DNA within the fibre. Our findings suggest that SYCP3 deposition on the chromosome axis might take place by polymerization into a fibre that is fastened to the chromosome surface via DNA binding.
DOI: 10.6084/m9.figshare.9916730.v1
title: Coordinate Shifts from Molecular architecture of the SYCP3 fibre and its interaction with DNA

description: The synaptonemal complex (SC) keeps homologous chromosomes in close alignment during meiotic recombination. A hallmark of the SC is the presence of its constituent protein SYCP3 on the chromosome axis. During SC assembly, SYCP3 is deposited on both axes of the homologue pair, forming axial elements that fuse into the lateral element (LE) in the tripartite structure of the mature SC. We have used cryo-electron tomography and atomic force microscopy to study the mechanism of assembly and DNA binding of the SYCP3 fibre. We find that the three-dimensional architecture of the fibre is built on a highly irregular arrangement of SYCP3 molecules displaying very limited local geometry. Interaction between SYCP3 molecules is driven by the intrinsically disordered tails of the protein, with no contact between the helical cores, resulting in a flexible fibre assembly. We demonstrate that the SYCP3 fibre can engage in extensive interactions with DNA, indicative of an efficient mechanism for incorporation of DNA within the fibre. Our findings suggest that SYCP3 deposition on the chromosome axis might take place by polymerization into a fibre that is fastened to the chromosome surface via DNA binding.
DOI: 10.6084/m9.figshare.9916751.v1
title: gaussian_csv.zip

description: Gaussian Finder's cavity dataset in CVS. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916745.v1
title: Gaussian Finder's cavity dataset in CSV

description: Gaussian Finder's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916745.v3
title: Gaussian Finder's cavity dataset in CSV

description: Gaussian Finder's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916745.v2
title: gaussian_csv.zip

description: Gaussian Finder's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Gaussian Finder. This method is described in the article available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1913-4<br><br>
DOI: 10.6084/m9.figshare.9916673
title: Fpocket's cavity dataset in XML

description: Fpocket's cavity dataset in XML. This dataset describes the protein cavities output by a protein cavity detection method called Fpocket. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2700099/<br><br>
DOI: 10.6084/m9.figshare.9916679
title: Supplementary Table-S1.xlsx

description: Supplementary Table-S1.Note: Detailed information of the 333 acetylated lysine sites identified in titin. All sites are found in titin (Gene name: Ttn) (Protein accession: A2ASS6) and amino acid type of all sites is K (Lysine). Positions: modification site localization in protein. Localization probability: Probability of a certain amino acid containing this modification in this peptide. PEP: The maximal posterior error probability for peptides. Score: A simple rule to be used to judge whether a result is significant or not. Modified sequence: Identified peptide sequence marked with modification sites localization probabilities. Mass error (ppm): Parts per million ratio of peptide mass error between theory and practice. MS/MS count: Number of spectrum matched with peptide. C1, C2, C3: numbers of gastrocnemius samples from control (C) group mice. E1, E2, E3: numbers of gastrocnemius samples from exercise (E) group. S1, S2, S3: numbers of gastrocnemius samples from Scriptaid (S) group.<br>
DOI: 10.6084/m9.figshare.9916670
title: Fpocket's cavity dataset in CVS

description: Fpocket's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Fpocket. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2700099/
DOI: 10.6084/m9.figshare.9916679.v1
title: Supplementary Table-S1.xlsx

description: Supplementary Table-S1.Note: Detailed information of the 333 acetylated lysine sites identified in titin. All sites are found in titin (Gene name: Ttn) (Protein accession: A2ASS6) and amino acid type of all sites is K (Lysine). Positions: modification site localization in protein. Localization probability: Probability of a certain amino acid containing this modification in this peptide. PEP: The maximal posterior error probability for peptides. Score: A simple rule to be used to judge whether a result is significant or not. Modified sequence: Identified peptide sequence marked with modification sites localization probabilities. Mass error (ppm): Parts per million ratio of peptide mass error between theory and practice. MS/MS count: Number of spectrum matched with peptide. C1, C2, C3: numbers of gastrocnemius samples from control (C) group mice. E1, E2, E3: numbers of gastrocnemius samples from exercise (E) group. S1, S2, S3: numbers of gastrocnemius samples from Scriptaid (S) group.<br>
DOI: 10.6084/m9.figshare.9916649
title: Data: A meta-analysis of crop response patterns to nitrogen limitation for improved model representation

description: Data used in "A meta-analysis of crop response patterns to nitrogen limitation for improved model representation". <br><br>R script to reproduce the results published in PLOSONE.<br><br><br>
DOI: 10.6084/m9.figshare.9916670.v2
title: Fpocket's cavity dataset in CVS

description: Fpocket's cavity dataset in CSV. This dataset describes the protein cavities output by a protein cavity detection method called Fpocket. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2700099/
DOI: 10.6084/m9.figshare.9916673.v2
title: Fpocket's cavity dataset in XML

description: Fpocket's cavity dataset in XML. This dataset describes the protein cavities output by a protein cavity detection method called Fpocket. This method is described in the article available at:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2700099/<br><br>
DOI: 10.6084/m9.figshare.9916673.v1
title: fpocket_xml.zip

description: Fpocket's cavity dataset in XML.
DOI: 10.6084/m9.figshare.9916670.v1
title: fpocket_csv.zip

description: Fpocket's cavity dataset in CSV
DOI: 10.6084/m9.figshare.9916649.v1
title: Data: A meta-analysis of crop response patterns to nitrogen limitation for improved model representation

description: Data used in "A meta-analysis of crop response patterns to nitrogen limitation for improved model representation". <br>