cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3671323
title: dad_f16

description: DAD F16 test data
DOI: 10.5281/zenodo.3671324
title: dad_f16

description: DAD F16 test data
DOI: 10.5281/zenodo.3671375
title: dad_coil100

description: DAD COIL100 test data
DOI: 10.5281/zenodo.3671376
title: dad_coil100

description: DAD COIL100 test data
DOI: 10.5281/zenodo.3671387
title: dad_dice

description: DAD Dice test data
DOI: 10.5281/zenodo.3671388
title: dad_dice

description: DAD Dice test data
DOI: 10.7910/dvn/jerj0c/gxifuz
title: comment_summarizing_income_mobility.R

description: :unav
DOI: 10.7295/w9cil50766
title: CIL:50766, Drosophila melanogaster, L1 larval brain
DOI: 10.15468/cw72oc
title: A new tyrannosaurine (Theropoda: Tyrannosauridae) from the Campanian Foremost Formation of Alberta, Canada, provides insight into the evolution and biogeography of tyrannosaurids

description: This dataset contains the digitized treatments in Plazi based on the original journal article Voris, Jared T., Therrien, François, Zelenitsky, Darla K., Brown, Caleb M. (2020): A new tyrannosaurine (Theropoda: Tyrannosauridae) from the Campanian Foremost Formation of Alberta, Canada, provides insight into the evolution and biogeography of tyrannosaurids. Cretaceous Research 110 (104388): 1-15, DOI: 10.1016/j.cretres.2020.104388
DOI: 10.7295/w9cil50767
title: CIL:50767, Drosophila melanogaster, L1 larval brain
DOI: 10.6073/pasta/055863323411e9a932ce1767b5d0810c
title: GIS50 Coverages Defining the Konza Producer Data Sample Locations (1982-present)

description: These data show the sample locations for datasets pertaining to primary production at Konza Prairie. These data reference various treatments across Konza including varying burn frequencies, belowground plots, patch burn, exclosures, etc.Record type one (GIS500) contains sample locations for estimated standing crop biomass in various burning-grazing treatments (PABXX). Record type six (GIS505) contains sample locations for peak foliage biomass measured at the belowground plot experiments (PBBXX).Record type 11 and 12 contain the transect (GIS510) and plot (GIS511) locations for plots in the patch-burn experiments (PBGXX). Record type 16 (GIS515) contains the locations of exclosures used to sample primary productivity in bison grazed watersheds (PEB01). Record type 21 (GIS520) contains the locations of exclosures used to sample primary productivity in cattle grazed watersheds (PEB01X). Record type 26 (GIS525) contains the locations of sample sites for litterfall (PGLXX) collectors in the gallery forest. Record type 31 (GIS530) contains species composition transects, and (GIS531) provides locations for species transect plots in the patch-burn experiments for Konza Prairie. These data may be used in conjunction species composition (PVC01 and PVC02), primary production in grazing exclosures (PEB01, PEB01_X), soil chemistry and bulk density (NSC01) and primary production (PAB01). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
DOI: 10.14469/hpc/6780
title: 5,8-di-tert-butyl-1,2,3,3-tetrakis(2,4,6-triisopropylphenyl)-5,8-diaza-1,2,3,4-tetrasilaspiro[3.4]oct-1-ene, singlet, G= -4003.442168, compound 4a.

description: Gaussian Calculation
DOI: 10.5519/qd.nmfzjlix
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.7295/w9cil50768
title: CIL:50768, Drosophila melanogaster, L1 larval brain
DOI: 10.7295/w9cil50769
title: CIL:50769, Drosophila melanogaster, L1 larval brain
DOI: 10.7295/w9cil50770
title: CIL:50770, Drosophila melanogaster, L1 larval brain
DOI: 10.5281/zenodo.3672045
title: Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation"

description: Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation": - Data of the incident radiation profiles in the annular reactor. - Average and net values of main radiation magnitudes in the tubular reactor at different photocatalyst concentration
DOI: 10.5281/zenodo.3672046
title: Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation"

description: Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation": - Data of the incident radiation radiation profiles in the annular reactor.
DOI: 10.7295/w9cil50771
title: CIL:50771, Drosophila melanogaster, L1 larval brain
DOI: 10.7295/w9cil50772
title: CIL:50772, Drosophila melanogaster, L1 larval brain
DOI: 10.7295/w9cil50773
title: CIL:50773, Drosophila melanogaster, L1 larval brain
DOI: 10.34619/d41y-ws04
title: A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes [Datasets]

description: Data from the manuscript: Smallbone K, Messiha HL, Carroll KM, et al. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett. 2013;587(17):2832-2841. doi:10.1016/j.febslet.2013.06.043
DOI: 10.17632/pxhkmn36dz.1
title: Data for: Variability in sociodemographic factors and obesity in Korean children: A cross-sectional analysis of Korea National Health and Nutrition Examination Survey data (2007–2015)
DOI: 10.25345/c5gq4m
title: MassIVE MSV000084962 - ARIH2 is a Vif-dependent regulator of CUL5-mediated APOBEC3G degradation in HIV infection: SRM-ELOB
title: SRM assays were generated for selected interactors of ELOB. SRM assay generation was performed using Skyline. For all targeted proteins, proteotypic peptides and optimal transitions for identification and quantification were selected based on the Skyline spectral library generated from the shotgun MS experiments (MSV000084855). For each protein, 2-5 peptides were selected based on intensity, peptide length as well as chromatographic performance. For each peptide the 4 best SRM tran- sitions were selected based on intensity and peak shape. Digested peptide mixtures were analyzed by LC-SRM on a Thermo Scientific TSQ Quantiva MS system equipped with a Proxeon. Easy nLC 1200 ultra high-pressure liquid chromatography and autosampler system. Samples were injected onto a C18 column (25 cm x 75 mm I.D. packed with ReproSil Pur C18 AQ 1.9 mm particles) in 0.1% formic acid and then separated with an 80 min gradient from 5% to 40% Buffer B (90% ACN/10% water/0.1% formic acid) at a flow rate of 300 nL/min. SRM acquisition was per- formed operating Q1 and Q3 at 0.7 unit mass resolution. For each peptide the best 4 transitions were monitored in a scheduled fashion with a retention time window of 4 min and a cycle time fixed to 2 seconds. Argon was used as the collision gas at a nominal pressure of 1.5 mTorr. Collision energies were calculated by, CE = 0.0348 * (m/z) + 0.4551 and CE = 0.0271 * (m/z) + 1.5910 (CE, collision energy and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. RF lens voltages were calculated by, RF = 0.1088 * (m/z) + 21.029 and RF = 0.1157 * (m/z) + 0.1157 (RF, RF lens voltage and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. The resulting data was analyzed with Skyline for identification and quantification of peptides. MSstats was used for statistical analysis.
DOI: 10.6073/pasta/edd4ed44a9b3428eb516b87e54e836cd
title: Periphyton Biomass Accumulation from the Shark River and Taylor Sloughs, Everglades National Park (FCE), from January 2003 to Present

description: Periphyton biomass was measured bimonthly at FCE LTER sites in Shark Slough. Fifteen 9.8 cm2 cores were removed from the floating periphyton mat and composited into a single sample. Percent aerial cover of floating periphyton was estimated visually in the field. Data are presented in terms of grams dry and ash-free dry mass per area. Total phosphorus, nitrogen and carbon concentration in the periphyton mat is also presented on a dry weight basis. This is part of a continuous data collection to test the hypothesis that phosphorus and nitrogen limit productivity at the upstream and downstream ends, respectively, of the FCE transects. Conclusions await longer-term data collection.
DOI: 10.5281/zenodo.3672134
title: PERFORM Dataset; one control subject

description: Functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), sleep and nutrition assessments were performed on one male control subject. This registered access dataset is currently accessible on request only.
DOI: 10.34619/m32r-1q02
title: Global analysis of protein expression in yeast [Datasets]

description: Data from the manuscript: Ghaemmaghami S, Huh WK, Bower K, et al. Global analysis of protein expression in yeast. Nature. 2003;425(6959):737-741. doi:10.1038/nature02046
DOI: 10.5281/zenodo.3672072
title: Gini index mean score grain size estimates for Murray formation rocks in the Vera Rubin ridge (Gale crater, Mars) from ChemCam LIBS data (sols 1808-2298)

description: Data in this repository are included as main research results in the Journal of Geophysical Research: Planets manuscripts entitled: <em>"</em>Extensive diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars<em>" </em> "A lacustrine paleoenvironment recorded at Vera Rubin ridge, Gale crater: Overview of the sedimentology and stratigraphy observed by the Mars Science Laboratory Curiosity rover". <strong>Table captions:</strong> <em>Table 1. </em>Murray formation targets from the Vera Rubin ridge used in the Gini mean index score (GIMS) analysis (sols 1808-2298) with summary information and G<sub>MEAN </sub>values with associated standard deviation errors. The grain size regimes (GSRs) were defined during the calibration procedure in Rivera-Hernández et al. (2020) and are defined as: mud (G<sub>MEAN</sub>=0.00-0.07; GSR1) and coarse silt to very fine sand (G<sub>MEAN</sub>=0.07-0.10; GSR2). Rocks with G<sub>MEAN</sub>=0.07 are exactly at GSR1 and GSR2 boundary and are reported as GSR1/GSR2. Next to the target names, the symbol * denotes that the ChemCam target was imaged by the Mars Hand Lens Imager (MAHLI), the symbol ** denotes that the dust removal tool was used before the MAHLI image was taken, and ~ signifies that a location close to the ChemCam target was imaged by the MAHLI. <em>Table 2</em>. The mean, median, minimum and maximum G<sub>MEAN</sub> and the minimum and maximum grain size regime (GSR) for each Murray formation locality in the Vera Rubin ridge. References: Rivera-Hernández, F., Sumner, D. Y., Mangold N., Wiens, R.W., Edgett, K., Fedo, C., Schieber, J., Banham, S.G., Newsom, H., Gupta, S., Heydari, E., Stack, K.M., Nachon, M., Stein, N., & Maurice, S. (2020) Grain Size Variations in the Murray Formation: Stratigraphic Evidence for Changing Depositional Environments in Gale Crater, Mars. <em>Journal of Geophysical Research:</em><em> Planets.</em> doi:10.1029/2019JE006230
DOI: 10.5281/zenodo.3672073
title: Gini index mean score grain size estimates for Murray formation rocks in the Vera Rubin ridge (Gale crater, Mars) from ChemCam LIBS data (sols 1808-2298)

description: Data in this repository are included as main research results in the Journal of Geophysical Research: Planets manuscripts entitled: <em>"</em>Extensive diagenesis revealed by fine-scale features at Vera Rubin ridge, Gale crater, Mars<em>" </em> "A lacustrine paleoenvironment recorded at Vera Rubin ridge, Gale crater: Overview of the sedimentology and stratigraphy observed by the Mars Science Laboratory Curiosity rover". <strong>Table captions:</strong> <em>Table 1. </em>Murray formation targets from the Vera Rubin ridge used in the Gini mean index score (GIMS) analysis (sols 1808-2298) with summary information and G<sub>MEAN </sub>values with associated standard deviation errors. The grain size regimes (GSRs) were defined during the calibration procedure in Rivera-Hernández et al. (2020) and are defined as: mud (G<sub>MEAN</sub>=0.00-0.07; GSR1) and coarse silt to very fine sand (G<sub>MEAN</sub>=0.07-0.10; GSR2). Rocks with G<sub>MEAN</sub>=0.07 are exactly at GSR1 and GSR2 boundary and are reported as GSR1/GSR2. Next to the target names, the symbol * denotes that the ChemCam target was imaged by the Mars Hand Lens Imager (MAHLI), the symbol ** denotes that the dust removal tool was used before the MAHLI image was taken, and ~ signifies that a location close to the ChemCam target was imaged by the MAHLI. <em>Table 2</em>. The mean, median, minimum and maximum G<sub>MEAN</sub> and the minimum and maximum grain size regime (GSR) for each Murray formation locality in the Vera Rubin ridge. References: Rivera-Hernández, F., Sumner, D. Y., Mangold N., Wiens, R.W., Edgett, K., Fedo, C., Schieber, J., Banham, S.G., Newsom, H., Gupta, S., Heydari, E., Stack, K.M., Nachon, M., Stein, N., & Maurice, S. (2020) Grain Size Variations in the Murray Formation: Stratigraphic Evidence for Changing Depositional Environments in Gale Crater, Mars. <em>Journal of Geophysical Research:</em><em> Planets.</em> doi:10.1029/2019JE006230
DOI: 10.25345/c5bx0x
title: MassIVE MSV000084963 - ARIH2 is a Vif-dependent regulator of CUL5-mediated APOBEC3G degradation in HIV infection: SRM-CUL5
title: SRM assays were generated for selected interactors of CUL5. SRM assay generation was performed using Skyline. For all targeted proteins, proteotypic peptides and optimal transitions for identification and quantification were selected based on the Skyline spectral library generated from the shotgun MS experiments (MSV000084855). For each protein, 2-5 peptides were selected based on intensity, peptide length as well as chromatographic performance. For each peptide the 4 best SRM transitions were selected based on intensity and peak shape. Digested peptide mixtures were analyzed by LC-SRM on a Thermo Scientific TSQ Quantiva MS system equipped with a Proxeon. Easy nLC 1200 ultra high-pressure liquid chromatography and autosampler system. Samples were injected onto a C18 column (25 cm x 75 mm I.D. packed with ReproSil Pur C18 AQ 1.9 mm particles) in 0.1% formic acid and then separated with an 80 min gradient from 5% to 40% Buffer B (90% ACN/10% water/0.1% formic acid) at a flow rate of 300 nL/min. SRM acquisition was per- formed operating Q1 and Q3 at 0.7 unit mass resolution. For each peptide the best 4 transitions were monitored in a scheduled fashion with a retention time window of 4 min and a cycle time fixed to 2 seconds. Argon was used as the collision gas at a nominal pressure of 1.5 mTorr. Collision energies were calculated by, CE = 0.0348 * (m/z) + 0.4551 and CE = 0.0271 * (m/z) + 1.5910 (CE, collision energy and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. RF lens voltages were calculated by, RF = 0.1088 * (m/z) + 21.029 and RF = 0.1157 * (m/z) + 0.1157 (RF, RF lens voltage and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. The resulting data was analyzed with Skyline for identification and quantification of peptides. MSstats was used for statistical analysis.
DOI: 10.25345/c57703
title: MassIVE MSV000084964 - ARIH2 is a Vif-dependent regulator of CUL5-mediated APOBEC3G degradation in HIV infection: SRM-CBFB
title: SRM assays were generated for selected interactors of CBFB. SRM assay generation was performed using Skyline. For all targeted proteins, proteotypic peptides and optimal transitions for identification and quantification were selected based on the Skyline spectral library generated from the shotgun MS experiments (MSV000084855). For each protein, 2-5 peptides were selected based on intensity, peptide length as well as chromatographic performance. For each peptide the 4 best SRM transitions were selected based on intensity and peak shape. Digested peptide mixtures were analyzed by LC-SRM on a Thermo Scientific TSQ Quantiva MS system equipped with a Proxeon. Easy nLC 1200 ultra high-pressure liquid chromatography and autosampler system. Samples were injected onto a C18 column (25 cm x 75 mm I.D. packed with ReproSil Pur C18 AQ 1.9 mm particles) in 0.1% formic acid and then separated with an 80 min gradient from 5% to 40% Buffer B (90% ACN/10% water/0.1% formic acid) at a flow rate of 300 nL/min. SRM acquisition was per- formed operating Q1 and Q3 at 0.7 unit mass resolution. For each peptide the best 4 transitions were monitored in a scheduled fashion with a retention time window of 4 min and a cycle time fixed to 2 seconds. Argon was used as the collision gas at a nominal pressure of 1.5 mTorr. Collision energies were calculated by, CE = 0.0348 * (m/z) + 0.4551 and CE = 0.0271 * (m/z) + 1.5910 (CE, collision energy and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. RF lens voltages were calculated by, RF = 0.1088 * (m/z) + 21.029 and RF = 0.1157 * (m/z) + 0.1157 (RF, RF lens voltage and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. The resulting data was analyzed with Skyline for identification and quantification of peptides. MSstats was used for statistical analysis.
DOI: 10.5281/zenodo.3672150
title: Simulation dataset (Project: Spectrum of Public Transit Operations: From Fixed Route to Microtransit)

description: This dataset is generated for the simulation of three different mobility services along the MTA Bus route B63 in Brooklyn, NY. Each includes the information of origin and destination of passengers within the service area and reflects different levels of demand (80, 200, and 400 passenger/hr). The dataset is produced based on MATLAB.
DOI: 10.25345/c53d6v
title: MassIVE MSV000084965 - ARIH2 is a Vif-dependent regulator of CUL5-mediated APOBEC3G degradation in HIV infection: SRM_CBFB_CUL2_CUL5_interactors
title: SRM assays were generated for selected interactors of CUL5, CBFB and ELOB. SRM assay generation was performed using Skyline. For all targeted proteins, proteotypic peptides and optimal transitions for identification and quantification were selected based on the Skyline spectral library generated from the shotgun MS experiments (MSV000084855). For each protein 2-5 peptides were selected based on intensity, peptide length as well as chromatographic performance. For each peptide the 4 best SRM transitions were selected based on intensity and peak shape. Digested peptide mixtures were analyzed by LC-SRM on a Thermo Scientific TSQ Quantiva MS system equipped with a Proxeon. Easy nLC 1200 ultra high-pressure liquid chromatography and autosampler system. Samples were injected onto a C18 column (25 cm x 75 mm I.D. packed with ReproSil Pur C18 AQ 1.9 mm particles) in 0.1% formic acid and then separated with an 80 min gradient from 5% to 40% Buffer B (90% ACN/10% water/0.1% formic acid) at a flow rate of 300 nL/min. SRM acquisition was per- formed operating Q1 and Q3 at 0.7 unit mass resolution. For each peptide the best 4 transitions were monitored in a scheduled fashion with a retention time window of 4 min and a cycle time fixed to 2 seconds. Argon was used as the collision gas at a nominal pressure of 1.5 mTorr. Collision energies were calculated by, CE = 0.0348 * (m/z) + 0.4551 and CE = 0.0271 * (m/z) + 1.5910 (CE, collision energy and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. RF lens voltages were calculated by, RF = 0.1088 * (m/z) + 21.029 and RF = 0.1157 * (m/z) + 0.1157 (RF, RF lens voltage and m/z, mass to charge ratio) for doubly and triply charged precursor ions, respectively. The resulting data was analyzed with Skyline for identification and quantification of peptides. MSstats was used for statistical analysis.
DOI: 10.21227/r73n-qw16
title: Facies-Mark: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.21227/az1d-w583
title: Facies-Mark: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.5281/zenodo.3672158
title: GC-MS data set for Generation of a chromosome-scale genome assembly of the insect-repellant terpenoid-producing Lamiaceae species, Callicarpa americana

description: RAW GC/MS data set for characterization of class II terpene synthases from <em>Callicarpa americana </em>
DOI: 10.6084/m9.figshare.11859750.v15
title: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment r1i1p1f1

description: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment (CO2 abruptly quadrupled and then held constant) r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.