cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13012/b2idb-1016367_v1
title: Dataset for On the Importance of Firth Bias Reduction in Few-Shot Classification

description: This data repository includes the features and the trained backbone parameters used in the ICLR 2022 Paper "On the Importance of Firth Bias Reduction in Few-Shot Classification". The code accompanying this data is open-source and available at https://github.com/ehsansaleh/firth_bias_reduction The code and the data have three modules: 1. The "code_firth" module (10 files) relates to the basic ResNet backbones and logistic classifiers (e.g., Figures 2 and 3 in the main paper). 2. The "code_s2m2rf" module (2 files) relates to the S2M2R feature backbones and cosine classifiers (e.g., Figure 4 in the main paper). 3. The "code_dcf" module (3 files) relates to the few-shot Distribution Calibration (DC) method (e.g., Table 1 in the main paper). The relevant files for each module have the module name as a prefix in their name. 1. For instance, the "code_dcf_features.tar" file should be placed at the "features" directory of the "code_dcf" module. 2. As another example, "code_firth_features_cifarfs_novel.tar" should be placed in the "features" directory of the "code_firth" module, and it includes the features extracted from the novel split of mini-ImageNet dataset. Each tar-ball should be extracted in its relevant directory, and the md5 check-sums of the extracted files are also provided in the open-source code repository for verification. Please note that the actual datasets of images are not included here (since we do not own those datasets). However, helper scripts for automatically downloading the original datasets are also provided in the every module and sub-directory of the GitHub code repository.
DOI: 10.6073/pasta/083c12a1452105f7e10bd53861e2e4e3
title: Rodent abundance and biomass data across grassland-shrubland ecotones at 3 sites in the Jornada Basin, 2004-ongoing

description: This is an example dataset for testing `jerald` and the Jornada IM system using dataset 210262010. Data here come from R’s `mtcars` example dataset. You can replace this abstract with one for your dataset.
DOI: 10.5066/p9tqims7
title: Estimated crop irrigation water use withdrawals in Butler Valley Groundwater Basin, Arizona for 2020

description: Observations of irrigated agricultural land within the Butler Valley Groundwater Basin in Arizona. Crops were verified in situ twice in 2020 first on March 11th and again on August 11th; based on digitized field boundaries. Field boundaries were digitized from U.S. Department of Agriculture, National Agricultural Imagery Program images dated 2019 and supplemented with Landsat and Sentinel2 imagery collections accessed via the U.S. Geological Survey LandLook Viewer (https://landlook.usgs.gov/) and Sentinel Hub, Sentinel Playground (https://apps.sentinel-hub.com/sentinel-playground). Satellite images were also used to identify the length of the growing season and crop condition. Water withdrawals were calculated using the modified Blaney-Criddle [...]
DOI: 10.5281/zenodo.6415289
title: Supplementary Data & Software for "Balancing the marine sulfur cycle", Johnson & Adkins (submitted)

description: These files are the supplementary data and software for the manuscript "Balancing the marine sulfur cycle" by Johnson & Adkins (submitted). The MATLAB workspace file "DeepSeaSO4_ClusterAnalysisData.mat" contains organized data structures with DSDP, ODP, and IODP site metadata, porosity data, pore water sulfate concentration data, methane concentration data, total organic carbon wt% data, and calcium carbonate wt% data + their corresponding depths. These data served as the basis for the cluster analysis performed by the included MATLAB script "SO4_conc_cluster_analysis.m" in the study. The "DatasetS1.xlsx" spreadsheet file contains all compiled pyrite sulfur isotopic data, pyrite abundance data, and total sulfur abundance data included within the literature compilation discussed in the manuscript. Pyrite sulfur isotopic data references are listed and are cited in the main text. DOI URLs for the total sulfur abundance data from the PANGAEA repository are also included in the spreadsheet. These data have additionally been saved into the MATLAB workspace files "Pyrite_d34S_Comp.mat" and "Pyrite Abundance_Comp.mat" for import into MATLAB by other scripts. The MATLAB workspace file "POROSITY.mat" includes vectors of latitude (in degrees), longitude (in degrees), water depth (in mbsl), sediment depth (in mbsf), porosity (in vol%), and corresponding PANGAEA DOI URLs along with the data tables from which this information was extracted. This file is called by "PorosityPlotter.m" to extract initial porosity values, compile coring methods, and make plots of initial porosity as a function of water depth for each coring method. The MATLAB script "hypsometry.m" loads the global topographic data included in "topography.mat", calculates associated surface areas across different ocean depth intervals, and determines average porosity and sedimentary pyrite parameters across those intervals in tandem with "S_Iso_Mass.m". "S_Iso_Mass.m" uses the averages to calculate the steady-state sulfur isotopic composition of seawater. The script "phi_waterdepth_analysis.m" loads the cluster analysis data and uses the corresponding porosity data to calculate linear regressions between initial porosity and water depth for use by other scripts; the workspace files "phi_waterdepth_fit.mat" and "phi_waterdepth_trimmed_fit.mat" are saved versions of the regressions used in the manuscript and called by "hypsometry.m" for calculating averages. The additional included files "PyriteSAbundanceCompilationSources.pdf" and "PorosityCompilationSources.pdf" files include full citations to the data included in the pyrite S abundance and porosity compilations (respectively).
DOI: 10.5281/zenodo.6415288
title: Supplementary Data & Software for "Balancing the marine sulfur cycle", Johnson & Adkins (submitted)

description: These files are the supplementary data and software for the manuscript "Balancing the marine sulfur cycle" by Johnson & Adkins (submitted). The MATLAB workspace file "DeepSeaSO4_ClusterAnalysisData.mat" contains organized data structures with DSDP, ODP, and IODP site metadata, porosity data, pore water sulfate concentration data, methane concentration data, total organic carbon wt% data, and calcium carbonate wt% data + their corresponding depths. These data served as the basis for the cluster analysis performed by the included MATLAB script "SO4_conc_cluster_analysis.m" in the study. The "DatasetS1.xlsx" spreadsheet file contains all compiled pyrite sulfur isotopic data, pyrite abundance data, and total sulfur abundance data included within the literature compilation discussed in the manuscript. Pyrite sulfur isotopic data references are listed and are cited in the main text. DOI URLs for the total sulfur abundance data from the PANGAEA repository are also included in the spreadsheet. These data have additionally been saved into the MATLAB workspace files "Pyrite_d34S_Comp.mat" and "Pyrite Abundance_Comp.mat" for import into MATLAB by other scripts. The MATLAB workspace file "POROSITY.mat" includes vectors of latitude (in degrees), longitude (in degrees), water depth (in mbsl), sediment depth (in mbsf), porosity (in vol%), and corresponding PANGAEA DOI URLs along with the data tables from which this information was extracted. This file is called by "PorosityPlotter.m" to extract initial porosity values, compile coring methods, and make plots of initial porosity as a function of water depth for each coring method. The MATLAB script "hypsometry.m" loads the global topographic data included in "topography.mat", calculates associated surface areas across different ocean depth intervals, and determines average porosity and sedimentary pyrite parameters across those intervals in tandem with "S_Iso_Mass.m". "S_Iso_Mass.m" uses the averages to calculate the steady-state sulfur isotopic composition of seawater. The script "phi_waterdepth_analysis.m" loads the cluster analysis data and uses the corresponding porosity data to calculate linear regressions between initial porosity and water depth for use by other scripts; the workspace files "phi_waterdepth_fit.mat" and "phi_waterdepth_trimmed_fit.mat" are saved versions of the regressions used in the manuscript and called by "hypsometry.m" for calculating averages. The additional included files "PyriteSAbundanceCompilationSources.pdf" and "PorosityCompilationSources.pdf" files include full citations to the data included in the pyrite S abundance and porosity compilations (respectively).
DOI: 10.5281/zenodo.6471470
title: SciExpeM - 10.5281/zenodo.6471470

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366
DOI: 10.5281/zenodo.6471469
title: SciExpeM - 10.5281/zenodo.6471470

description: Heufer, K.A.; Fernandes, R.X.; Olivier, H.; Beeckmann, J.; Röhl, O.; Peters, N. - Proceedings Of The Combustion Institute, 2011, (33), 359-366
DOI: 10.57687/fk2/pwjrsj/7qtpbo
title: Student Dropout Dictionary_Data teaser.xlsx

description: :unav
DOI: 10.6084/m9.figshare.16985734.v2
title: Supplemental Material.docx

description: Supplemental materials for "Treadmill running increases the calcium sensitivity of myofilaments in diabetic rats." Shows baseline body weight data and more details regarding training protocol used in this study.<br>
DOI: 10.6084/m9.figshare.16985734
title: Supplemental Material.docx

description: Supplemental materials for "Treadmill running increases the calcium sensitivity of myofilaments in diabetic rats." Shows baseline body weight data and more details regarding training protocol used in this study.<br>
DOI: 10.6084/m9.figshare.16985734.v1
title: Supplemental Material.docx

description: Supplemental materials for "Treadmill running increases the calcium sensitivity of myofilaments in diabetic rats." Shows baseline body weight data and more details regarding training protocol used in this study.<br>
DOI: 10.6069/rjm0-cs62
title: HDL Proteomics in Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study

description: Atherosclerotic cardiovascular disease (CVD) is the major cause of death in patients with type 1 diabetes mellitus (T1DM). Alterations in the HDL proteome associate with prevalent CVD in T1DM. We therefore determined which proteins carried by HDL might predict incident CVD in T1DM patients. Using targeted MS/MS, we quantified 50 proteins in HDL from 181 T1DM subjects enrolled in the prospective Coronary Artery Calcification in Type 1 Diabetes study (CACTI). We used Cox proportional regression analysis and a case-cohort design to test associations of HDL proteins with incident CVD (myocardial infarction, coronary artery bypass grafting, angioplasty, or death from coronary heart disease). Only one HDL protein—SFTPB (pulmonary surfactant protein B)—predicted incident CVD in all of the models tested. In a fully adjusted model that controlled for lipids and other risk factors, the hazard ratio was 2.17 per SD increase of SFTPB (95% confidence interval, 1.12-4.21, P=0.022). Plasma fractionation demonstrated that plasma SFTPB is nearly quantitatively bound to HDL. Although previous studies have shown that high plasma levels of SFTPB associate with prevalent atherosclerosis only in smokers, we found that SFTPB predicted incident CVD in T1DM independently of smoking status. Elevated levels of SFTPB in HDL strongly predicted incident CVD in CACTI subjects independently of a wide range of confounding factors, including smoking status, HDL-C, LDL-C, and triglyceride levels. Because SFTPB is almost quantitatively bound to plasma HDL, our observations support the proposal that SFTPB carried by HDL is a marker—and perhaps mediator—of CVD risk in patients with T1DM.
DOI: 10.6084/m9.figshare.19375088.v1
title: Herpes simplex virus

description: this data obtained from pregnant women in wadmadani Sudan<br>
DOI: 10.5281/zenodo.6459852
title: Autism-linked UBE3A gain-of-function mutation causes interneuron and behavioral phenotypes when inherited maternally or paternally in mice

description: The E3 ubiquitin ligase Ube3a is biallelically expressed in mitotic cells, including neural progenitors and glial cells, raising the possibility that UBE3A gain-of-function mutations might cause neurodevelopmental disorders irrespective of parent-of-origin. To test this possibility, we engineered a mouse line that harbors an autism-linked UBE3A-T485A (T508A in mouse) gain-of-function mutation and evaluated phenotypes in animals that inherited the mutant allele paternally, maternally, or from both parents. We found that both paternally and maternally expressed UBE3A-T485A resulted in elevated UBE3A activity in neural progenitors and glial cells where Ube3a is biallelically expressed. Expression of UBE3A-T485A from the maternal allele, but not the paternal one, led to a persistent elevation of UBE3A activity in postmitotic neurons. Maternal, paternal, or biparental inheritance of the mutant allele promoted embryonic expansion of Zcchc12 lineage interneurons which mature into Sst and Calb2 expressing interneurons, and caused a spectrum of behavioral phenotypes that differed by parent-of-origin. Phenotypes were distinct from those observed in Angelman syndrome model mice that harbor a Ube3a maternal loss-of-function allele. Our study shows that the UBE3A-T485A gain-of-function mutation causes distinct neurodevelopmental phenotypes when inherited maternally or paternally. These findings have clinical implications for a growing number of disease-linked UBE3A gain-of-function mutations. Single-cell RNA-seq (drop-seq) of mouse cerebral cortex from Ube3a-WT, Ube3a-T485A homozygotes, assessed at two ages: E14.5 and P0
DOI: 10.5281/zenodo.6459851
title: Autism-linked UBE3A gain-of-function mutation causes interneuron and behavioral phenotypes when inherited maternally or paternally in mice

description: The E3 ubiquitin ligase Ube3a is biallelically expressed in mitotic cells, including neural progenitors and glial cells, raising the possibility that UBE3A gain-of-function mutations might cause neurodevelopmental disorders irrespective of parent-of-origin. To test this possibility, we engineered a mouse line that harbors an autism-linked UBE3A-T485A (T508A in mouse) gain-of-function mutation and evaluated phenotypes in animals that inherited the mutant allele paternally, maternally, or from both parents. We found that both paternally and maternally expressed UBE3A-T485A resulted in elevated UBE3A activity in neural progenitors and glial cells where Ube3a is biallelically expressed. Expression of UBE3A-T485A from the maternal allele, but not the paternal one, led to a persistent elevation of UBE3A activity in postmitotic neurons. Maternal, paternal, or biparental inheritance of the mutant allele promoted embryonic expansion of Zcchc12 lineage interneurons which mature into Sst and Calb2 expressing interneurons, and caused a spectrum of behavioral phenotypes that differed by parent-of-origin. Phenotypes were distinct from those observed in Angelman syndrome model mice that harbor a Ube3a maternal loss-of-function allele. Our study shows that the UBE3A-T485A gain-of-function mutation causes distinct neurodevelopmental phenotypes when inherited maternally or paternally. These findings have clinical implications for a growing number of disease-linked UBE3A gain-of-function mutations. Single-cell RNA-seq (drop-seq) of mouse cerebral cortex from Ube3a-WT, Ube3a-T485A homozygotes, assessed at two ages: E14.5 and P0
DOI: 10.5281/zenodo.6471527
title: VCF data for genetic polyploid phasing

description: Used data for evaluation in pending Recomb 2022 submission "Genetic Polyploid Phasing using Marker Signals from Low-Depth Progeny Samples". Data was used in and partly created by the scripts from here: https://github.com/AlBi-HHU/genetic-phasing-scripts
DOI: 10.5281/zenodo.5645742
title: VCF data for genetic polyploid phasing

description: Used data for evaluation in pending Recomb 2022 submission "Genetic Polyploid Phasing using Marker Signals from Low-Depth Progeny Samples". Data was used in and partly created by the scripts from here: https://github.com/AlBi-HHU/genetic-phasing-scripts
DOI: 10.17632/d64kvw6cv7.1
title: Values of Chinese PV cells imports and exports and the Grubel-Lloyd index with the BRI coun-tries in 2019.

description: Data used in the research titled: “The Chinese photovoltaic cells industry and the Belt and Road Initiative – the intra-industry perspective.”
Source: own elaboration. List of countries sourced from Nedopil, C. (2020) Countries of the Belt and Road Initiative. Beijing: IIGF Green BRI Center, https://www.green-bri.org. Data sourced from UN COMTRADE database (https://comtrade.un.org/data/)(Accessed: the 10th of April 2021).
DOI: 10.17632/y8dfhc6fmn.1
title: Automatic cyst and kidney segmentation in autosomal dominant polycystic kidney disease

description: Detailed figures with sample images and segmentation results.
DOI: 10.6084/m9.figshare.19617732
title: Plasma membrane proteins are modulated after vaccinia virus-infection

description: HFFF-TERTs or HeLa cells were mock-treated or infected with VACV at MOI 5 in DMEM supplemented with 2% FCS, 1% P/S. Cells were harvested at 15-18 hpi using accutase solution following the manufacturer’s instructions (Sigma-Aldrich). Validation of PM protein expression was performed with ~2x10^5 cells /sample, stained with Zombie NIR Fixable Viability Kit (Biolegend) in combination with the following antibodies: MHC-I (W6/32, kindly provided by Dr L. Boyle), MICA (2C10, Santa Cruz Biotechnology, Santa Cruz, CA, USA), EPHB4 (rea923, Miltenyi Biotech, Bergisch Gladbach, Germany), FAS(CD95) (DX2, Miltenyi Biotech), EGFR (528, Santa Cruz), HLA-B/C (4E, kindly provided by Dr L. Boyle), HLA-C/E (DT9, kindly provided by Dr L. Boyle), ULBP3 (166510, R&D systems), B7-H6 (875001, R&D systems, Minneapolis, U.S.A), ULBP-2/5/6 (65903, R&D systems), Plexin-B1 (rea728, Miltenyi Biotech), MICB (236511, R&D systems), TNFSRF10D (trail-R4) (104918, R&D systems), CD155 (SKII.4, Santa Cruz), isotype mouse Igg2b (Santa Cruz), isotype mouse IgG2a (Santa Cruz), isotype mouse IgG1 (Santa Cruz). Samples stained with unconjugated primary antibodies were subsequently probed with PE-conjugated Goat anti-mouse IgG (BioLegend). Cells were fixed using Fixation/Permeabilization Solution Kit according to the manufacturer’s instructions (BD Biosciences). Samples were analysed by flow cytometry using an Invitrogen Attune NxT cytometer and FlowJo software.
DOI: 10.6084/m9.figshare.19617894
title: Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño

description: All these data and codes are used for the analysis of "Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño".
DOI: 10.34740/kaggle/ds/1818627
title: Monthly Orders for snacks-Munchies Application
DOI: 10.6084/m9.figshare.19534285
title: Metadata of soils in Spartina alterniflora salt marshes and their adjacent mudflats along coastlines of China

description: 407 soil samples from 12 sites were collected along the coastlines of China. Soil physicochemical properties were measured according to standard procedures, and climatic conditions were obtained from WorldClim version 2 (https://www.worldclim.org/) at 30 arc-second resolutions.
DOI: 10.6084/m9.figshare.19534285.v3
title: Metadata of soils in Spartina alterniflora salt marshes and their adjacent mudflats along coastlines of China

description: 407 soil samples from 12 sites were collected along the coastlines of China. Soil physicochemical properties were measured according to standard procedures, and climatic conditions were obtained from WorldClim version 2 (https://www.worldclim.org/) at 30 arc-second resolutions.
DOI: 10.6084/m9.figshare.19612983.v1
title: Dynamic distribution and prevention of spontaneous combustion in roof cutting gob-side entry retaining goaf of the first mining face

description: <br> oftadaDynamic distribution and prevention of spontaneous combustion in roof cutting gob-side entry retaining goaf of the first mining face
DOI: 10.6084/m9.figshare.19617918.v1
title: Epuck-00000000.txt

description: raw data
DOI: 10.5281/zenodo.6469286
title: Simulations of Slantwise Critical Reflection

description: Realistic and idealized simulations of slantwise critical reflection.
DOI: 10.5281/zenodo.6469285
title: Simulations of Slantwise Critical Reflection

description: Realistic and idealized simulations of slantwise critical reflection.
DOI: 10.5281/zenodo.6471045
title: Sample Beiwe Dataset

description: This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe. As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".
description: NIH / NIHLBI - Grant Number: 5U01HL145386
DOI: 10.5281/zenodo.6471044
title: Sample Beiwe Dataset

description: This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe. As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".
description: NIH / NIHLBI - Grant Number: 5U01HL145386
DOI: 10.6084/m9.figshare.19607190
title: Supplementary Dataset 1

description: <strong>The diversity of quinoa morphological traits and seed metabolic composition</strong> Iman Tabatabaei, Saleh Alseekh, Mohammad Shahid, Ewa Leniak, Mateusz Wagner, Henda Mahmoudi, Sumitha Thushar, Alisdair R. Fernie, Kevin M. Murphy, Sandra M. Schmöckel, Mark Tester, Bernd Mueller-Roeber, Aleksandra Skirycz, Salma Balazadeh
DOI: 10.6084/m9.figshare.19607190.v1
title: Supplementary Dataset 1

description: <strong>The diversity of quinoa morphological traits and seed metabolic composition</strong> Iman Tabatabaei, Saleh Alseekh, Mohammad Shahid, Ewa Leniak, Mateusz Wagner, Henda Mahmoudi, Sumitha Thushar, Alisdair R. Fernie, Kevin M. Murphy, Sandra M. Schmöckel, Mark Tester, Bernd Mueller-Roeber, Aleksandra Skirycz, Salma Balazadeh