cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5v40k08j
title: MassIVE Reanalysis RMSV000000354.4394 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ATP1B4 [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/c5kk94d1k
title: MassIVE MSV000088890 - Vigna unguiculata CE31 roots quantitative control vs infected with Meloidogyne incognita vs drought vs cross-stress
title: 1234567890 1234567890 1234567890 1234567890 1234567890 1234567890 1234567890
DOI: 10.6084/m9.figshare.19214169
title: RT-qPCR data

description: <b>Melt-curve plots.pdf</b>: qPCR melt curve plots of SRPK2 and CHDH generated using the QuantStudio 3 Real-time PCR software. The melt curves are generated by plotting Derivative Reporter (-Rn′) versus Temperature (°C).<br><br>SRPK2 Quetiapine.eds, SRPK2 Valproate.eds, CHDH Lamotrigine.eds, CHDH Quetiapine.eds: raw eds files generated by QuantStudio 3 Real-time PCR platform (Thermo Fisher Scientific)
DOI: 10.25345/c5fx73z85
title: MassIVE Reanalysis RMSV000000354.4395 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CPN1 [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.19208538
title: Dataset included in study Elvén, M.; Kerstis, B.; Stier, J.; Hellström, C.; von Heideken Wågert, P.; Dahlen, M.; Lindberg, D. Changes in Physical Activity and Sedentary Behavior before and during the COVID-19 Pandemic: A Swedish Population Study. Int. J. Environ. Res. Public Health 2022, 19, x

description: Dataset including variables of physical activity and sedentary behavior during the COVID-19 pandemic (December 2020) in the Swedish population.
DOI: 10.3886/e163161v1
title: Statutory Insurance & Workers’ Welfare

description: This project asks if statutory accident insurance was the starting point for the exceptional expansion of the Swedish welfare state. Our study shows voluntary welfare protection was an integral part of the early-twentieth century welfare system and played a crucial role in protecting workers in the case of sickness and accident; and continued to do so well after the introduction of statutory accident insurance in 1916. Although under-insurance was the main motive for introducing statutory insurance, the government viewed over-insurance through voluntary insurance among workers as a disciplinary problem, due to the risk of moral hazard, both before and after the introduction of statutory accident insurance in 1916. Voluntary initiatives continued to expand throughout the period and were not crowded out by statutory accident insurance. However, statutory insurance did reduce wage increases and the self-insurance capabilities of better paid workers in high-risk industries.
DOI: 10.5519/qd.pmiuetse
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (21 records)
DOI: 10.25345/c5b56d557
title: MassIVE Reanalysis RMSV000000354.4396 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, PA2G4 [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.6226621
title: Data for the publication "Low sensitivity of three terrestrial biosphere models to soil texture over the South-American tropics"

description: Code and data for the aforementionned publication
DOI: 10.25345/c56d5pc13
title: MassIVE Reanalysis RMSV000000354.4397 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, BCAR3 [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.6136989
title: bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial

description: Downsampled version of the melanoma dataset originally published by <em>Ho et al (1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available: <strong>Name</strong> <strong>Description</strong> <strong>R1/R2</strong> 451LU Parental cell line 2500K_451LU_L003_R*_001.fastq.gz 451LUBR3 Vemurafenib-resistant sample treated with targeted BRAF inhibitors 500K_451LUBR3_L004_R*_001.fastq.gz <br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117
description: The original single-cell data can be accessed through the Gene Expression Omnibus (GEO), accession number GSE108394.
description: {"references": ["Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117", "Garc\u00eda-Jimeno, L., Fustero-Torre, C., Jim\u00e9nez-Santos, M. J., G\u00f3mez-L\u00f3pez, G., Di Domenico, T., & Al-Shahrour, F. (2022). bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses. Bioinformatics, 38(4), 1155-1156."]}
DOI: 10.5281/zenodo.1403159
title: bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial

description: Downsampled version of the melanoma dataset originally published by <em>Ho et al (1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available: <strong>Name</strong> <strong>Description</strong> <strong>R1/R2</strong> 451LU Parental cell line 2500K_451LU_L003_R*_001.fastq.gz 451LUBR3 Vemurafenib-resistant sample treated with targeted BRAF inhibitors 500K_451LUBR3_L004_R*_001.fastq.gz <br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117
description: The original single-cell data can be accessed through the Gene Expression Omnibus (GEO), accession number GSE108394.
description: {"references": ["Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117", "Garc\u00eda-Jimeno, L., Fustero-Torre, C., Jim\u00e9nez-Santos, M. J., G\u00f3mez-L\u00f3pez, G., Di Domenico, T., & Al-Shahrour, F. (2022). bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses. Bioinformatics, 38(4), 1155-1156."]}
DOI: 10.5281/zenodo.6226693
title: Dati estratti da "Epidemia COVID-19", bollettino dell'Istituto Superiore di Sanità (ISS)

description: <br> “EPIDEMIA COVID-19” è un bollettino prodotto dall’Istituto Superiore di Sanità (ISS) che riporta i dati della sorveglianza integrata concernente l’infezione da virus SARS-CoV-2. Il bollettino è redatto ai sensi dell’Ordinanza n. 640 del 27 febbraio 2020. Si legge nella nota introduttiva che “Il conteggio dei casi viene effettuato sommando tutti i casi di infezione da virus SARS-CoV2 confermati tramite test molecolari e antigenici, e segnalati al sistema di sorveglianza attraverso una piattaforma web dedicata. Il database nazionale è quindi la somma dei casi presenti nei 21 sistemi informativi regionali/PPAA costruiti ad hoc per la sorveglianza COVID-19”. Si riportano di seguito grafici di dati estratti dai rapporti, concernenti casistica su persone di età ≥ 12 anni, ricoveri e vittime. Le tabelle indicano anche lo stato vaccinale e la fascia d’età. Si presti attenzione che le classi relative alla vaccinazione sono state ridefinite a seconda dei Decreti Legge del Governo. I dati sono estratti partendo dal bollettino del 10 Novembre 2021 fino a quelli del 9 Febbraio 2022. Si noti che i dati riportati risultano dal conteggio sull’arco temporale di un mese. I bollettini differiscono per lo spostamento di una settimana. L’inizio dell’arco temporale è in ritardo di un mese per i contagi ma di più di un mese per gli altri dati. Il primo bollettino considerato è quello 10-Novembre-2021 che riporta le diagnosi di contagio dal 08/10/2021 al 07/11/2021, il numero delle vittime tra il 17/09/2021 al 17/10/2021, e i dati di ospedalizzazioni e terapie intensive dal 01/10/2021 al 31/10/2021. Il bollettino più recente è quello 16-Febbraio-2022, che contiene i dati delle diagnosi di contagio tra il 14 Gennaio 2022 ed il 13 Febbraio 2022,<br> delle vittime tra il 24/12/2021 al 23/01/2022, delle ospedalizzazioni 31/12/2021 al 30/01/2022, delle terapie intensive 31/12/2021 al 30/01/2022.
DOI: 10.25345/c52n4zj7b
title: MassIVE Reanalysis RMSV000000354.4398 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ACVRL1 [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.6226519
title: Harpyhaliaetus solitarius SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6226518
title: Harpyhaliaetus solitarius SPAdes preassembly

description: .
DOI: 10.25345/c5xw47x2b
title: MassIVE Reanalysis RMSV000000354.4399 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ALDH2 [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/c5t43j389
title: MassIVE Reanalysis RMSV000000354.4400 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CCL4 [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.6226765
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>
DOI: 10.25345/c5pc2t95n
title: MassIVE Reanalysis RMSV000000354.4401 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, GBE1 [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.5599743
title: Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia

description: Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM<sub>2.5</sub>) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be substantial contributors. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) plausibly also contribute to the SOOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.
DOI: 10.5281/zenodo.5599742
title: Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia

description: Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM<sub>2.5</sub>) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be substantial contributors. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) plausibly also contribute to the SOOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.
DOI: 10.21227/vn0e-cw64
title: CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging: Datasets

description: TODO
DOI: 10.25345/c5jq0sw2v
title: MassIVE Reanalysis RMSV000000354.4402 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, DRD2 [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/c5dz0329c
title: MassIVE Reanalysis RMSV000000354.4403 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ZNF23 [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/c5959c84h
title: MassIVE Reanalysis RMSV000000354.4404 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, EXOSC1 [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/c55d8ng0c
title: MassIVE Reanalysis RMSV000000354.4405 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, RHOD [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.6213188
title: SuperDARN data in netCDF format (2017-Dec)

description: 2017-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6211431
title: SuperDARN data in netCDF format (2018-Jun)

description: 2018-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'