cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5g73748z
title: MassIVE Reanalysis RMSV000000354.4512 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MMP28 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.6084/m9.figshare.6394385
title: Data for: Bohenek, J. R., and W. J. Resetarits Jr. 2018. Are direct density cues, not resource competition, driving life history trajectories in a polyphenic salamander? Evolutionary Ecology 32:335–357.

description: Data for "Are direct density cues, not resource competition, driving life history trajectories in a polyphenic salamander?"<br>Bohenek, J.R. & Resetarits, W.J. Evol Ecol (2018). https://doi.org/10.1007/s10682-018-9941-8<br>See metadata for comprehensive info.<br>Short description:<br>We investigated proximate cues affecting facultative paedomorphosis in order to understand how larval newts (Notophthalmus viridescens louisianensis) assess conspecific density. To isolate the effects of density cues from the effects of resources and agonistic behavior, we caged larval newts in mesocosms in a 2 × 2 factorial design that manipulated both background larval newt densities (high or low) and food levels (ambient or supplemented).<br>
DOI: 10.6084/m9.figshare.6394385.v3
title: Are direct density cues, not resource competition, driving life history trajectories in a polyphenic salamander?

description: Data for "Are direct density cues, not resource competition, driving life history trajectories in a polyphenic salamander?"<br>Bohenek, J.R. & Resetarits, W.J. Evol Ecol (2018). https://doi.org/10.1007/s10682-018-9941-8<br>See metadata for comprehensive info.
DOI: 10.5519/qd.qihcdg9z
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (11222 records)
DOI: 10.5517/ccdc.csd.cc29c360
title: CCDC 2125831: Experimental Crystal Structure Determination

description: Related Article: Wenchao Yan, Zelun Cai, Hao Qi, Ruoyao Guo, Zhiwei Liu, Zuqiang Bian|2022|Dalton Trans.|51|3234|doi:10.1039/D1DT04072C
DOI: 10.5517/ccdc.csd.cc29c371
title: CCDC 2125832: Experimental Crystal Structure Determination

description: Related Article: Wenchao Yan, Zelun Cai, Hao Qi, Ruoyao Guo, Zhiwei Liu, Zuqiang Bian|2022|Dalton Trans.|51|3234|doi:10.1039/D1DT04072C
DOI: 10.25345/c5bg2hb59
title: MassIVE Reanalysis RMSV000000354.4515 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, THAP2 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c56t0gx2h
title: MassIVE Reanalysis RMSV000000354.4516 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CRYGC [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5319s38x
title: MassIVE Reanalysis RMSV000000354.4513 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, AKNAD1 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.23641/asha.19191278
title: Task and language in Spanish–English narratives (Wofford et al., 2022)

description: <b>Purpose: </b>This study examined performance of dual language learners (DLLs) on Spanish- and English-language narrative story retells and unique tells. Transcription and analysis focused on comparisons of common microstructural language sample measures in Spanish and English across tasks. Each language sample measure was evaluated for its possible convergence with norm-referenced standardized assessments for DLL children.<b>Method: </b>Spanish–English DLLs (<i>N</i> = 133) enrolled in English-only kindergarten or first-grade classrooms completed two-language sample tasks (one in each language), which were transcribed and analyzed using Systematic Analysis of Language Transcripts (Miller & Iglesias, 2017) for measures of syntactic complexity (mean length of utterance [MLU] in words), lexical diversity (number of different words [NDW]), and grammaticality (percent grammatical utterances [PGU]). Students also completed a norm-referenced sentence repetition task (Peña et al., 2014) and expressive vocabulary assessment (Martin, 2013).<b>Results:</b> Comparison of story retells and unique stories revealed similar performance on MLU, NDW, and PGU across elicitation techniques, with one exception: NDW in Spanish was higher in the story retell condition. Predictive models revealed several differences in the relations between the microstructure measures and norm-referenced language measures by elicitation technique, although neither context demonstrated a consistent advantage across all metrics.<b>Conclusions: </b>Measures derived from story retells and unique tells offer practical findings for speech-language pathologists (SLPs) and other educators to use in assessment of early grade DLLs. This work increases knowledge of procedural differences across narrative assessments and their influence on language variables, supporting school-based SLPs in making assessment decisions for DLLs on their caseload.<br><b>Supplemental Material S1. </b>Descriptive information for the sample by language and elicitation technique.<br><b>Supplemental Material S2. </b>Unstandardized differences by elicitation technique in Spanish, controlling for age & TNU. <br><b>Supplemental Material S3.</b> Unstandardized differences by elicitation technique in English, controlling for age & TNU.<br><b>Supplemental Material S4. </b>Estimates from <i>Z</i>-scored LSA predictors and language outcome measures.<br>Wofford, M. C., Cano, J., Goodrich, J. M., & Fitton, L. (2022). Tell or retell? The role of task and language in Spanish–English narrative microstructure performance. <i>Language, Speech, and Hearing Services in Schools</i>. Advance online publication. https://doi.org/10.1044/2021_LSHSS-21-00055
DOI: 10.23641/asha.19191278.v1
title: Task and language in Spanish–English narratives (Wofford et al., 2022)

description: <b>Purpose: </b>This study examined performance of dual language learners (DLLs) on Spanish- and English-language narrative story retells and unique tells. Transcription and analysis focused on comparisons of common microstructural language sample measures in Spanish and English across tasks. Each language sample measure was evaluated for its possible convergence with norm-referenced standardized assessments for DLL children.<b>Method: </b>Spanish–English DLLs (<i>N</i> = 133) enrolled in English-only kindergarten or first-grade classrooms completed two-language sample tasks (one in each language), which were transcribed and analyzed using Systematic Analysis of Language Transcripts (Miller & Iglesias, 2017) for measures of syntactic complexity (mean length of utterance [MLU] in words), lexical diversity (number of different words [NDW]), and grammaticality (percent grammatical utterances [PGU]). Students also completed a norm-referenced sentence repetition task (Peña et al., 2014) and expressive vocabulary assessment (Martin, 2013).<b>Results:</b> Comparison of story retells and unique stories revealed similar performance on MLU, NDW, and PGU across elicitation techniques, with one exception: NDW in Spanish was higher in the story retell condition. Predictive models revealed several differences in the relations between the microstructure measures and norm-referenced language measures by elicitation technique, although neither context demonstrated a consistent advantage across all metrics.<b>Conclusions: </b>Measures derived from story retells and unique tells offer practical findings for speech-language pathologists (SLPs) and other educators to use in assessment of early grade DLLs. This work increases knowledge of procedural differences across narrative assessments and their influence on language variables, supporting school-based SLPs in making assessment decisions for DLLs on their caseload.<br><b>Supplemental Material S1. </b>Descriptive information for the sample by language and elicitation technique.<br><b>Supplemental Material S2. </b>Unstandardized differences by elicitation technique in Spanish, controlling for age & TNU. <br><b>Supplemental Material S3.</b> Unstandardized differences by elicitation technique in English, controlling for age & TNU.<br><b>Supplemental Material S4. </b>Estimates from <i>Z</i>-scored LSA predictors and language outcome measures.<br>Wofford, M. C., Cano, J., Goodrich, J. M., & Fitton, L. (2022). Tell or retell? The role of task and language in Spanish–English narrative microstructure performance. <i>Language, Speech, and Hearing Services in Schools</i>. Advance online publication. https://doi.org/10.1044/2021_LSHSS-21-00055
DOI: 10.3886/e161561v1
title: Supporting change in instructional practices to meet the Common Core Mathematics and Next Generation Science Standards

description: Included are instructions on how to obtain data and program files for the AERAOpen article, <b>Supporting change in instructional practices to meet the Common Core Mathematics and Next Generation Science Standards: How are different supports related to instructional change?</b> <br><br><br><br><br>
DOI: 10.25345/c5z892g3x
title: MassIVE Reanalysis RMSV000000354.4514 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, DIP2A [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5th8bn95
title: MassIVE Reanalysis RMSV000000354.4518 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MYL2 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5pr7mv51
title: MassIVE Reanalysis RMSV000000354.4519 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, TMCC1 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5k06x223
title: MassIVE Reanalysis RMSV000000354.4517 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, FADS3 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.5706315
title: GitTables benchmark - column type detection

description: <strong>Note: the download page of the entire GitTables corpus is here: https://zenodo.org/record/4943312.</strong> This dataset represents a <strong>small subset</strong> of tables from GitTables curated for benchmarking column type detection methods. This benchmark evaluates systems that match table columns to semantic types from the DBpedia and Schema.org ontologies. It is featured in the SemTab 2021 challenge (CTA task). This dataset consists of the following files: “tables.zip”: directory with a sample of 1101 tables from GitTables. Filenames correspond to table IDs, the first column (without column name) corresponds to row indices, column names are replaced with "col_0", "col_1", etc. which match to the targets and labels (semantic types). “&lt;ontology&gt;_targets.csv”: target columns per table ID, 1 file per ontology (DBpedia or Schema.org): columns are “table_id” (ignore the "_&lt;ontology&gt;" suffix) and “target_column” (i.e. the column that should be annotated). “&lt;ontology&gt;_gt.csv”: ground truth column annotations per table ID, 1 file per ontology: columns are “table_id” (ignore the _&lt;ontology&gt; suffix), “target_column”, “annotation_id”, “annotation_label”. “&lt;ontology&gt;_labels.csv”: unique labels present in the annotated tables, 1 file per ontology: columns are “annotation_id” and “annotation_label”. The labels (semantic types) from each ontology come from: DBpedia: only properties from the DBpedia ontology. Schema.org: properties and types from Schema.org. For the entire GitTables corpus, please refer to this dataset. Visit https://gittables.github.io for more background and contact details.
DOI: 10.25345/c5fb4wn0s
title: MassIVE Reanalysis RMSV000000354.4520 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CD48 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c59k45t7w
title: MassIVE Reanalysis RMSV000000354.4502 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ALG5 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c55t3g135
title: MassIVE Reanalysis RMSV000000354.4523 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MRPL39 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5222r69q
title: MassIVE Reanalysis RMSV000000354.4524 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, C17orf82 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5x921k4q
title: MassIVE Reanalysis RMSV000000354.4525 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MAP2K5 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5sj19s0k
title: MassIVE Reanalysis RMSV000000354.4511 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, FDPS [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.23645/epacomptox.6328037.v7
title: ToxCast and Tox21 High-throughput Screening Assay Description Documentation

description: ToxCast and Tox21 High-throughput Screening Assay Descriptions.
DOI: 10.25345/c5ns0kz79
title: MassIVE Reanalysis RMSV000000354.4526 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, GTSF1 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5j38kj4p
title: MassIVE Reanalysis RMSV000000354.4521 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ZNF397 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.6230123
title: msd-errors - Data for AgCrSe2 figures

description: Data for the figures Results of the analysis of AgCrSe2 systems by kinisi at a series of temperatures.<br><br>Created using showyourwork from this GitHub repo.<br>