cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4386316
title: InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning

description: LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth. Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes (belonging to 108 plant species), classified down to the lineage level. This data set has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall. In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to remove keep only intact elements: 1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia), 2) or elements with domains belonging to two or more different lineages, 3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%, 4) to delete incomplete elements which has less than three identified domains, and 5) to remove elements with insertions of TE class II (reported in Repbase). The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code: &gt;Superfamily-Lineage-plant_family-specie-source-length-ID, Where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family follows following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.
DOI: 10.5281/zenodo.6380316
title: Database of Last Interglacial sea level indicators from the Bahamas, Turks and Caicos, and the east coast of Florida, USA

description: This is a compilation of stratigraphic constraints and relevant U-series ages from the Last Interglacial period taken from samples in the Bahamas, Turks and Caicos, and the east coast of Florida, USA. It has been exported from the World Atlas of Last Interglacial Shorelines (WALIS) database (https://warmcoasts.eu/world-atlas.html).
DOI: 10.6084/m9.figshare.19404563
title: Tomator detection

description: tomator detection dataset
DOI: 10.6073/pasta/1ea463bba7db978190c114f2a7866866
title: Average monthly sea ice coverage for the PAL LTER region West of the Antarctic Peninsula derived from passive microwave, 1978 - July 2021

description: Monthly sea ice coverage derived from passive microwave satellite measurements. Ice extent is defined as the area bounded by the 15% ice concentration contour which is determined by summing up all the pixel areas (nominally 25km by 25km) which have concentrations greater than 15%. Sea-ice extent refers to the total surface area inside the ice-edge. This area inside the ice-edge is not necessarily covered by 100%.
DOI: 10.6073/pasta/561924d9a82cb31b520678ec022ff244
title: Average yearly sea ice coverage for the PAL LTER region West of the Antarctic Peninsula derived from passive microwave satellite, 1979 - 2020.

description: Annual sea ice coverage derived from monthly sea ice coverages originally derived from passive microwave satellite measurements. Ice extent is defined as the area bounded by the 15% ice concentration contour which is determined by summing up all the pixel areas (nominally 25km by 25km) which have concentrations greater than 15%. This area inside the ice-edge is not necessarily covered by 100%.
DOI: 10.6084/m9.figshare.19404581
title: S-table 1-2 diet and list of reagents and S-legends R3.docx

description: Table and figure legends
DOI: 10.6084/m9.figshare.19404581.v1
title: S-table 1-2 diet and list of reagents and S-legends R3.docx

description: Table and figure legends
DOI: 10.6084/m9.figshare.19236951
title: dataset

description: dataset
DOI: 10.6084/m9.figshare.19236951.v3
title: dataset

description: dataset
DOI: 10.6084/m9.figshare.19236951.v2
title: Achilles dataset 21Q2

description: Achilles dataset (version 21Q2), containing sgRNA-level fold-changes and pre-processed with CRISPRcleanR for the correction of gene-independent response to CRISPR-Cas9 targeting
DOI: 10.5281/zenodo.6380360
title: Monthly Normalized MAVEN Data (2014-2021)

description: These data were obtained from the magnetometer (MAG), Neutral Gas and Ion Mass Spectrometer (NGIMS), and Solar Wind Ion Analyzer (SWIA) onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. These two data sets were derived from these three instruments' data products and utilized in the article <strong>Influence of Magnetic Fields on Precipitating Solar Wind Hydrogen at Mars</strong>, which we plan to submit to Geophysical Research Letters. The backscattered and downward propagating penetrating proton data have been separated into two data files titled respectively. The contents of each file are as follows: 1. Time [s]: epoch time (seconds since Jan. 1, 1970) for each penetrating proton measurement 2. Orbit [#]: MAVEN's orbit number 3. LSM [degrees]: Martian solar longitude 4. Flux [eV/(eV s sr cm^2)]: peak angle averaged differential energy flux for each 4-s spectrum in electronvolt per electronvolt x second x steradian x square centimeters. 5. Normalized Flux: monthly normalized peak flux (unitless) 6. Energy [eV]: energy at which peak flux occurred for each 4-s spectrum found from Gaussian fit in electronvolts 7. FWHM [eV]: full width at half maximum of 4-s spectrum found from Gaussian fit in electronvolts 8. Altitude [km]: spacecraft altitude in kilometers 9. SZA [degrees]: solar zenith angle in degrees 10. X_MSO [km]: X position of MAVEN in the Mars-Sun-Orbit (MSO) coordinate system in kilometers 11. Y_MSO [km]: Y position of MAVEN in the MSO coordinate system in kilometers 12. Z_MSO [km]: Z position of MAVEN in the MSO coordinate system in kilometers 13. Latitude [degrees]: Martian planetary latitude 14. Longitude [degrees]: Martian planetary longitude 15. Bx [nT]: X component of magnetic field measured by MAG in MSO coordinates in nanotesla 16. By [nT]: Y component of magnetic field measured by MAG in MSO coordinates in nanotesla 17. Bz [nT]: Z component of magnetic field measured by MAG in MSO coordinates in nanotesla 18. Elevation Angle [degrees]: local magnetic field elevation angle measured relative to a vector tangent to the Martian surface during each penetrating proton measurement. 90 degree elevation angle corresponds to radial configurations while 0 degree elevation angle corresponds to horizontal configurations. 19. Column Density [cm^-2]: CO<sub>2</sub> column density derived from NGIMS measurements in inverse square centimeters. All NaN values were used to fill for times during which NGIMS inbound verified data were not available, and the column density could not be determined.
DOI: 10.5281/zenodo.6380359
title: Monthly Normalized MAVEN Data (2014-2021)

description: These data were obtained from the magnetometer (MAG), Neutral Gas and Ion Mass Spectrometer (NGIMS), and Solar Wind Ion Analyzer (SWIA) onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. These two data sets were derived from these three instruments' data products and utilized in the article <strong>Influence of Magnetic Fields on Precipitating Solar Wind Hydrogen at Mars</strong>, which we plan to submit to Geophysical Research Letters. The backscattered and downward propagating penetrating proton data have been separated into two data files titled respectively. The contents of each file are as follows: 1. Time [s]: epoch time (seconds since Jan. 1, 1970) for each penetrating proton measurement 2. Orbit [#]: MAVEN's orbit number 3. LSM [degrees]: Martian solar longitude 4. Flux [eV/(eV s sr cm^2)]: peak angle averaged differential energy flux for each 4-s spectrum in electronvolt per electronvolt x second x steradian x square centimeters. 5. Normalized Flux: monthly normalized peak flux (unitless) 6. Energy [eV]: energy at which peak flux occurred for each 4-s spectrum found from Gaussian fit in electronvolts 7. FWHM [eV]: full width at half maximum of 4-s spectrum found from Gaussian fit in electronvolts 8. Altitude [km]: spacecraft altitude in kilometers 9. SZA [degrees]: solar zenith angle in degrees 10. X_MSO [km]: X position of MAVEN in the Mars-Sun-Orbit (MSO) coordinate system in kilometers 11. Y_MSO [km]: Y position of MAVEN in the MSO coordinate system in kilometers 12. Z_MSO [km]: Z position of MAVEN in the MSO coordinate system in kilometers 13. Latitude [degrees]: Martian planetary latitude 14. Longitude [degrees]: Martian planetary longitude 15. Bx [nT]: X component of magnetic field measured by MAG in MSO coordinates in nanotesla 16. By [nT]: Y component of magnetic field measured by MAG in MSO coordinates in nanotesla 17. Bz [nT]: Z component of magnetic field measured by MAG in MSO coordinates in nanotesla 18. Elevation Angle [degrees]: local magnetic field elevation angle measured relative to a vector tangent to the Martian surface during each penetrating proton measurement. 90 degree elevation angle corresponds to radial configurations while 0 degree elevation angle corresponds to horizontal configurations. 19. Column Density [cm^-2]: CO<sub>2</sub> column density derived from NGIMS measurements in inverse square centimeters. All NaN values were used to fill for times during which NGIMS inbound verified data were not available, and the column density could not be determined.
DOI: 10.25345/c5q23r40r
title: MassIVE MSV000089108 - TARGETING LIPA INDEPENDENT OF ITS LIPASE ACTIVITY IS A THERAPEUTIC STRATEGY IN SOLID TUMORS VIA INDUCTION OF ENDOPLASMIC RETICULUM STRESS
title: pCW57-LIPA-TurboID transduced cells were treated with or without 1mg/mL doxycycline for 48h, then were treated with 50mM biotin for 15min and then harvested and lysed in RIPA buffer (Thermo Scientific) supplemented with Halt protease inhibitor cocktail (Thermo Scientific) and Phosphatase Inhibitor Cocktail set I and set II (MilliporeSigma) on ice for 15min, followed by 20min of 20,000g centrifuge at 4 degrees C. Cleared cell lysates were incubated with M-270 streptavidin Dynabeads overnight. Then the beads were washed twice with RIPA buffer (2min), once with 1M KCl (2min), once with 0.1M Na2CO3 (10s), once with 2M urea in 10mM Tris-HCl (pH 8.0) (10s), and twice with RIPA buffer (2min). Enriched material was eluted from beads by boiling samples in 4x Laemmli buffer supplemented with 2mM biotin and 20mM DTT at 95 degrees C for 10min. Eluted samples were resolved in SDS-PAGE and followed by mass spectrometry by Lumos. biological replicate 1, 929193 (clone 1, no Dox.), 929194 (clone 1, with Dox.), 929196 (clone 2, no Dox.) and 929197 (clone 2, with Dox.). biological replicate 2, 932804 (clone 1, no Dox.), 932805 (clone 1, with Dox.), 932807 (clone 2, no Dox.) and 932808 (clone 2, with Dox.).
DOI: 10.6084/m9.figshare.19398179
title: National Carbon Stocks

description: This dataset integrates both plant biomass and soil organic carbon into one data layer at a 250m resolution. Units are in kg of C and are based upon Sothe <i>et al.</i> 2022.<br><br>
DOI: 10.6084/m9.figshare.17278595
title: Metabolic Transformation of Fat in Obesity Determines the Inflammation Resolving Capacity of Splenocardiac and Cardiorenal Networks in Heart Failure

description: Chronic and surplus dietary intake of safflower oil (SO) increased plasma creatinine dysregulated post-MI splenocardiac inflammation coincides with the dysfunctional cardiorenal network. In contrast, docosahexaenoic acid (DHA) increases post-MI survival in chronic heart failure. DHA transforms to specialized proresolving mediators (SPMs) and limited proinflammatory prostaglandins and thromboxanes following myocardial infarction (MI). DHA promotes Ly6C<sup>low</sup> resolving macrophages and T regulatory cells (Foxp3<sup>+</sup>) in a splenocardiac manner post-MI. <b><i></i></b>
DOI: 10.6084/m9.figshare.17278595.v2
title: Metabolic Transformation of Fat in Obesity Determines the Inflammation Resolving Capacity of Splenocardiac and Cardiorenal Networks in Heart Failure

description: Chronic and surplus dietary intake of safflower oil (SO) increased plasma creatinine dysregulated post-MI splenocardiac inflammation coincides with the dysfunctional cardiorenal network. In contrast, docosahexaenoic acid (DHA) increases post-MI survival in chronic heart failure. DHA transforms to specialized proresolving mediators (SPMs) and limited proinflammatory prostaglandins and thromboxanes following myocardial infarction (MI). DHA promotes Ly6C<sup>low</sup> resolving macrophages and T regulatory cells (Foxp3<sup>+</sup>) in a splenocardiac manner post-MI. <b><i></i></b>
DOI: 10.6084/m9.figshare.17278595.v1
title: Metabolic Transformation of Fat in Obesity Determines the Inflammation Resolving Capacity of Splenocardiac and Cardiorenal Networks in Heart Failure

description: Chronic and surplus dietary intake of safflower oil (SO) increased plasma creatinine dysregulated post-MI splenocardiac inflammation coincides with the dysfunctional cardiorenal network. In contrast, docosahexaenoic acid (DHA) increases post-MI survival in chronic heart failure. DHA transforms to specialized proresolving mediators (SPMs) and limited proinflammatory prostaglandins and thromboxanes following myocardial infarction (MI). DHA promotes Ly6C<sup>low</sup> resolving macrophages and T regulatory cells (Foxp3<sup>+</sup>) in a splenocardiac manner post-MI. <b><i></i></b>
DOI: 10.5281/zenodo.6379087
title: Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"

description: Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity". Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)
DOI: 10.5281/zenodo.6378762
title: Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"

description: Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity". Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)
DOI: 10.5281/zenodo.6379344
title: Supporting Online Toxicity Detection with Knowledge Graphs: Data

description: This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022). The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO). We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).
DOI: 10.5281/zenodo.6379343
title: Supporting Online Toxicity Detection with Knowledge Graphs: Data

description: This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022). The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO). We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).
DOI: 10.5281/zenodo.6380439
title: The Potential of GNSS-R for Freeze/Thaw Surface State Monitoring in the New Space Era

description: <em><strong>GNSS-R based F/T surface state maps over the Tibet Plateau, Andes Mountains, and Rocky Mountains covering two full F/T cycles 2019-2020:</strong> </em> <em><strong>(a) </strong>Number of frozen months during 2019</em> <em><strong>(b)</strong> number of thawed months during 2019</em> <em><strong>(c) </strong>number of frozen months during 2020</em> <em><strong>(d)</strong> number of thawed months during 2020</em> <em><strong>(e) </strong>difference of the number of frozen months between 2019 and 2020</em> <em><strong>(f) </strong>difference of the number of thawed months between 2019 and 2020</em> <em><strong>(g) </strong>number of frozen months during the 2019 freezing period (September to December)</em> <em><strong>(h) </strong>number of thawed months during the 2019 thawing period (January to June)</em> <em><strong>(i) </strong>number of frozen months during the 2020 freezing period (September to December)</em> <em><strong>(j)</strong> number of thawed months during the 2020 thawing period (January to June)</em> <em><strong>(k) </strong>difference of the number of frozen months between 2019 and 2020 during the freezing period (September to December)</em> <em><strong>(l) </strong>difference of the number of thawed months between 2019 and 2020 during the thawing period (January to June)</em>
DOI: 10.5281/zenodo.6380438
title: The Potential of GNSS-R for Freeze/Thaw Surface State Monitoring in the New Space Era

description: <em><strong>GNSS-R based F/T surface state maps over the Tibet Plateau, Andes Mountains, and Rocky Mountains covering two full F/T cycles 2019-2020:</strong> </em> <em><strong>(a) </strong>Number of frozen months during 2019</em> <em><strong>(b)</strong> number of thawed months during 2019</em> <em><strong>(c) </strong>number of frozen months during 2020</em> <em><strong>(d)</strong> number of thawed months during 2020</em> <em><strong>(e) </strong>difference of the number of frozen months between 2019 and 2020</em> <em><strong>(f) </strong>difference of the number of thawed months between 2019 and 2020</em> <em><strong>(g) </strong>number of frozen months during the 2019 freezing period (September to December)</em> <em><strong>(h) </strong>number of thawed months during the 2019 thawing period (January to June)</em> <em><strong>(i) </strong>number of frozen months during the 2020 freezing period (September to December)</em> <em><strong>(j)</strong> number of thawed months during the 2020 thawing period (January to June)</em> <em><strong>(k) </strong>difference of the number of frozen months between 2019 and 2020 during the freezing period (September to December)</em> <em><strong>(l) </strong>difference of the number of thawed months between 2019 and 2020 during the thawing period (January to June)</em>
DOI: 10.25934/pr00004514.0
title: Available IPD datapackage for study 'Pharmacokinetic Evaluations of Ixekizumab Following Subcutaneous Administration Using Prefilled Syringe or Auto-Injector in Patients With Moderate-to-Severe Plaque Psoriasis'

description: The purpose of this study is to evaluate the serum concentration of ixekizumab after administration using either prefilled syringe or auto-injector in participants with moderate to severe plaque psoriasis. Treatment period is followed by 40 weeks optional safety extension.
DOI: 10.6073/pasta/3b74363b40f858a2dcbe1b83790aee77
title: A multi-year DAILY weather file for the Toolik Field Station at Toolik Lake, Alaska starting 1988 to present.

description: A multi-year DAILY weather file for the Arctic Tundra Long-Term Ecological Research (LTER) site at Toolik Lake, AK. Included are daily averages and/or maximums and minimums of air, wind speed, soil temperature, and sum of global radiation and precipitation. In 2008 Toolik Field Station took over maintenance of the main weather station. See http://toolik.alaska.edu/edc/index.php for current weather data. In addition to the main weather station the Arctic LTER maintains several stations that collect data on the experimental plots.
DOI: 10.5519/qd.qjv72xqc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (45 records)
DOI: 10.5517/ccdc.csd.cc225zlv
title: CCDC 1912377: Experimental Crystal Structure Determination

description: Related Article: Jaechul Lee, Younggyu Seo, Dong Won Kang, Seungjae Park, Hyunyong Kim, Jaheon Kim, Kimoon Kim, Chang Seop Hong, Dae-Woon Lim, Eunsung Lee|2022|RSC Advances|12|7605|doi:10.1039/D2RA01270G
DOI: 10.5517/ccdc.csd.cc25jrj0
title: CCDC 2012102: Experimental Crystal Structure Determination

description: Related Article: Julius F. Kögel, Denis A. Sorokin, Martin Scott, Klaus Harms, Daniel Himmel, Ingo Krossing, Jörg Sundermeyer|2022|Dalton Trans.|51|4829|doi:10.1039/D2DT00003B
DOI: 10.5517/ccdc.csd.cc25jrk1
title: CCDC 2012103: Experimental Crystal Structure Determination

description: Related Article: Julius F. Kögel, Denis A. Sorokin, Martin Scott, Klaus Harms, Daniel Himmel, Ingo Krossing, Jörg Sundermeyer|2022|Dalton Trans.|51|4829|doi:10.1039/D2DT00003B
DOI: 10.5517/ccdc.csd.cc25jrl2
title: CCDC 2012104: Experimental Crystal Structure Determination

description: Related Article: Julius F. Kögel, Denis A. Sorokin, Martin Scott, Klaus Harms, Daniel Himmel, Ingo Krossing, Jörg Sundermeyer|2022|Dalton Trans.|51|4829|doi:10.1039/D2DT00003B
DOI: 10.5517/ccdc.csd.cc25jrm3
title: CCDC 2012105: Experimental Crystal Structure Determination

description: Related Article: Julius F. Kögel, Denis A. Sorokin, Martin Scott, Klaus Harms, Daniel Himmel, Ingo Krossing, Jörg Sundermeyer|2022|Dalton Trans.|51|4829|doi:10.1039/D2DT00003B
DOI: 10.5517/ccdc.csd.cc25jrn4
title: CCDC 2012106: Experimental Crystal Structure Determination

description: Related Article: Julius F. Kögel, Denis A. Sorokin, Martin Scott, Klaus Harms, Daniel Himmel, Ingo Krossing, Jörg Sundermeyer|2022|Dalton Trans.|51|4829|doi:10.1039/D2DT00003B
DOI: 10.5517/ccdc.csd.cc299dvw
title: CCDC 2124208: Experimental Crystal Structure Determination

description: Related Article: Jamie S. Sweet, Ruichen Wang, Panagiotis Manesiotis, Paul Dingwall, Peter C. Knipe|2022|Org.Biomol.Chem.|20|2392|doi:10.1039/D2OB00177B
DOI: 10.5517/ccdc.csd.cc299dwx
title: CCDC 2124209: Experimental Crystal Structure Determination

description: Related Article: Jamie S. Sweet, Ruichen Wang, Panagiotis Manesiotis, Paul Dingwall, Peter C. Knipe|2022|Org.Biomol.Chem.|20|2392|doi:10.1039/D2OB00177B
DOI: 10.5517/ccdc.csd.cc299dxy
title: CCDC 2124210: Experimental Crystal Structure Determination

description: Related Article: Jamie S. Sweet, Ruichen Wang, Panagiotis Manesiotis, Paul Dingwall, Peter C. Knipe|2022|Org.Biomol.Chem.|20|2392|doi:10.1039/D2OB00177B
DOI: 10.5517/ccdc.csd.cc299dyz
title: CCDC 2124211: Experimental Crystal Structure Determination

description: Related Article: Jamie S. Sweet, Ruichen Wang, Panagiotis Manesiotis, Paul Dingwall, Peter C. Knipe|2022|Org.Biomol.Chem.|20|2392|doi:10.1039/D2OB00177B