cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6388302
title: PM2.5 emissions from Siberian forest fires 2004-2021

description: The dataset contains Supplementary Materials for the article <em>''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis''</em> in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis. <br> <br> <strong>Supplementary Figures</strong>:<br> - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line); - Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha. - Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map); - Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region; - Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic; - Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region; - Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory; - Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory. <br> We share Copernicus Atmosphere Monytoring Service <strong>PM2.5 emissions maps</strong> (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594. <br> To determine emissions from the territory of Russia, we provide <strong>shapefiles</strong> with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334). Also, there are<strong> initial dataset</strong> for analysis (Initital data_SARIMAX archive) and <strong>SARIMAX model settings</strong> (doc.).
DOI: 10.18112/openneuro.ds003766.v1.1.0
title: HD-EEG task with mouse tracking
DOI: 10.6084/m9.figshare.19607466
title: Additional file 1 of Early and strong antibody responses to SARS-CoV-2 predict disease severity in COVID-19 patients

description: Additional file 1: Table S1. Predicted SARS-CoV-2 epitopes expressed in-house. Table S2. Commercial SARS-CoV-2 proteins and human control proteins.
DOI: 10.6084/m9.figshare.19607466.v1
title: Additional file 1 of Early and strong antibody responses to SARS-CoV-2 predict disease severity in COVID-19 patients

description: Additional file 1: Table S1. Predicted SARS-CoV-2 epitopes expressed in-house. Table S2. Commercial SARS-CoV-2 proteins and human control proteins.
DOI: 10.6084/m9.figshare.19607496
title: Additional file 2 of Variation in targetable genomic alterations in non-small cell lung cancer by genetic ancestry, sex, smoking history, and histology

description: Additional file 2: Table S1. Master Table of clinicopathological characteristics of 3,115 patients with NSCLC sequenced at DFCI. Table S2. EGFRL858R/ex19del/ex20ins variants reported in patients with NSCLC sequenced at DFCI. Table S3. KRASG12C variants reported in patients with NSCLC sequenced at DFCI. Table S4. Exon 14 skipping mutations and/or amplifications in MET reported in patients with NSCLC sequenced at DFCI. Table S5. ALK fusions reported in patients with NSCLC sequenced at DFCI. Table S6. ROS1 fusions reported in patients with NSCLC sequenced at DFCI. Table S7. BRAFV600E variants reported in patients with NSCLC sequenced at DFCI. Table S8. RET fusions reported in patients with NSCLC sequenced at DFCI. Table S9. NTRK fusions reported in patients with NSCLC sequenced at DFCI. Table S10. Master Table of clinicopathological characteristics of 5,560 patients with NSCLC sequenced at one of three institutions (DCC, VICC, MSKCC). Table S11. Frequency of eight targetable alterations in DFCI LUAD patients by pack-year groups. Table S12. Comparison of targetable alteration frequencies in pack-year groups among patients with NSCLC at DFCI. Table S13. Distribution of targetable alterations by pack-year smoking history among patients with LUSC at DFCI. Table S14. Multivariable logistic regression model of eight targetable alterations, incorporating continental ancestry scores, age at diagnosis, smoking pack-year history, sex and histology DFCI cohort). Table S15. Comparison of KRASG12C mutation frequencies across continental ancestries (discovery cohort from DFCI) and self-reported racial groups (validation cohort from DCC, MSKCC, and VICC). Table S16. Multivariable logistic regression model of eight targetable alterations, incorporating sex and histology (validation cohort). Table S17. Tumor mutational burden and Ashkenazi Jewish ancestry assessment in TCGA LUAD and LUSC cohorts. Table S18. Multivariable logistic regression model of eight targetable alterations, incorporating Ashkenazi Jewish ancestry scores, age at diagnosis, smoking pack-year history, sex and histology.
DOI: 10.6084/m9.figshare.19607496.v1
title: Additional file 2 of Variation in targetable genomic alterations in non-small cell lung cancer by genetic ancestry, sex, smoking history, and histology

description: Additional file 2: Table S1. Master Table of clinicopathological characteristics of 3,115 patients with NSCLC sequenced at DFCI. Table S2. EGFRL858R/ex19del/ex20ins variants reported in patients with NSCLC sequenced at DFCI. Table S3. KRASG12C variants reported in patients with NSCLC sequenced at DFCI. Table S4. Exon 14 skipping mutations and/or amplifications in MET reported in patients with NSCLC sequenced at DFCI. Table S5. ALK fusions reported in patients with NSCLC sequenced at DFCI. Table S6. ROS1 fusions reported in patients with NSCLC sequenced at DFCI. Table S7. BRAFV600E variants reported in patients with NSCLC sequenced at DFCI. Table S8. RET fusions reported in patients with NSCLC sequenced at DFCI. Table S9. NTRK fusions reported in patients with NSCLC sequenced at DFCI. Table S10. Master Table of clinicopathological characteristics of 5,560 patients with NSCLC sequenced at one of three institutions (DCC, VICC, MSKCC). Table S11. Frequency of eight targetable alterations in DFCI LUAD patients by pack-year groups. Table S12. Comparison of targetable alteration frequencies in pack-year groups among patients with NSCLC at DFCI. Table S13. Distribution of targetable alterations by pack-year smoking history among patients with LUSC at DFCI. Table S14. Multivariable logistic regression model of eight targetable alterations, incorporating continental ancestry scores, age at diagnosis, smoking pack-year history, sex and histology DFCI cohort). Table S15. Comparison of KRASG12C mutation frequencies across continental ancestries (discovery cohort from DFCI) and self-reported racial groups (validation cohort from DCC, MSKCC, and VICC). Table S16. Multivariable logistic regression model of eight targetable alterations, incorporating sex and histology (validation cohort). Table S17. Tumor mutational burden and Ashkenazi Jewish ancestry assessment in TCGA LUAD and LUSC cohorts. Table S18. Multivariable logistic regression model of eight targetable alterations, incorporating Ashkenazi Jewish ancestry scores, age at diagnosis, smoking pack-year history, sex and histology.
DOI: 10.6084/m9.figshare.19607556
title: Additional file 1 of Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

description: Additional file 1. The contents of data mining.
DOI: 10.6084/m9.figshare.19607556.v1
title: Additional file 1 of Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

description: Additional file 1. The contents of data mining.
DOI: 10.6084/m9.figshare.19607592
title: Additional file 2 of Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

description: Additional file 2. The executable program file software.
DOI: 10.6084/m9.figshare.19607592.v1
title: Additional file 2 of Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

description: Additional file 2. The executable program file software.
DOI: 10.5281/zenodo.6465155
title: Hypoplectrus guttavarius decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465154
title: Hypoplectrus guttavarius decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465043
title: A Deep Neural Network Based SMAP Soil Moisture Product

description: It is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas -- when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.
DOI: 10.5281/zenodo.6465042
title: A Deep Neural Network Based SMAP Soil Moisture Product

description: It is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas -- when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.
DOI: 10.5281/zenodo.6463950
title: Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"

description: This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres.
DOI: 10.6084/m9.figshare.3761562.v2190
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5519/qd.ydjtgapm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (944760 records)
DOI: 10.5281/zenodo.6465230
title: Input data for ERIC values calculation for RAA process

description: For each area (in total 9 areas of SEE) and for each week (in total 2 weeks of 2021) used in final demonstration of CROSSBOW TC1.1.1 one input file is prepared – in total 18 excel files (*.xlsx). Each file is consisted of 4 sheets: LOAD – load forecast for 168 timestamps of the given week; DISP_GEN_THERMAL – unit capacity and Forced Outage Rate of production for dispatchable thermal units; DISP_GEN_HYDRO – unit capacity and Forced Outage Rate of production for dispatchable hydro units; NONDISP_GEN – forecasted values for Photovoltaic, Wind, Run of River, Combined Heat and Power and Biomass non-dispatchable units for 168 timestamps of the given week. Sampling period: Week 9 and Week 10 of 2021
DOI: 10.5281/zenodo.6465229
title: Input data for ERIC values calculation for RAA process

description: For each area (in total 9 areas of SEE) and for each week (in total 2 weeks of 2021) used in final demonstration of CROSSBOW TC1.1.1 one input file is prepared – in total 18 excel files (*.xlsx). Each file is consisted of 4 sheets: LOAD – load forecast for 168 timestamps of the given week; DISP_GEN_THERMAL – unit capacity and Forced Outage Rate of production for dispatchable thermal units; DISP_GEN_HYDRO – unit capacity and Forced Outage Rate of production for dispatchable hydro units; NONDISP_GEN – forecasted values for Photovoltaic, Wind, Run of River, Combined Heat and Power and Biomass non-dispatchable units for 168 timestamps of the given week. Sampling period: Week 9 and Week 10 of 2021
DOI: 10.5281/zenodo.6465218
title: CROSSBOW TC_01.01.01 final demonstration results

description: This data set represents final demonstration results for the Test Case 1.1 from the High Level Use case 1 of the CROSSBOW project. It contains information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME; MK, HR, RO and RS) and 366 timestamps (of Week 9 and Week 10 of 2021). All information about RAA methodology and terms used in this data set could be found in document <em>D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</em>
DOI: 10.5281/zenodo.6465217
title: CROSSBOW TC_01.01.01 final demonstration results

description: This data set represents final demonstration results for the Test Case 1.1 from the High Level Use case 1 of the CROSSBOW project. It contains information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME; MK, HR, RO and RS) and 366 timestamps (of Week 9 and Week 10 of 2021). All information about RAA methodology and terms used in this data set could be found in document <em>D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</em>
DOI: 10.5281/zenodo.6465263
title: Clean Apks - Androzoo

description: clean Android applications from Androzoo
DOI: 10.5281/zenodo.6433988
title: Clean Apks - Androzoo

description: clean Android applications from Androzoo
DOI: 10.5281/zenodo.6465261
title: Hypoplectrus nigricans decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465260
title: Hypoplectrus nigricans decontaminated FSCR

description: .
DOI: 10.48366/r178164
title: IoT dependencies extraction and modeling

description: Presents related work on IoT dependencies modeling and extraction
DOI: 10.5281/zenodo.6465271
title: Twindroid - System calls traces - Android apps - Extention

description: TwinDroid - a dataset of system calls traces, from both benign and infected Android apps. For other traces you can visit https://zenodo.org/record/6464808#.Yls2yVyZNH4 To trace apps, you can use our script in: https://github.com/RaphaelKhoury/automated-apk-tracing
DOI: 10.5281/zenodo.6465277
title: Hypoplectrus providencianus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465276
title: Hypoplectrus providencianus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6464808
title: Twindroid - System calls traces - Android apps

description: TwinDroid - a dataset of over 10 000 system calls traces, from both benign and infected Android apps. A large part of the dataset is composed of traces from pairs of benign and infected apps. For more traces you can visit https://zenodo.org/record/6465271#.Yls3F1yZNH4 To trace Android application, you can use our script in https://github.com/RaphaelKhoury/automated-apk-tracing
DOI: 10.5281/zenodo.5899795
title: Twindroid - System calls traces - Android apps

description: TwinDroid - a dataset of over 10 000 system calls traces, from both benign and infected Android apps. A large part of the dataset is composed of traces from pairs of benign and infected apps. For more traces you can visit https://zenodo.org/record/6465271#.Yls3F1yZNH4 To trace Android application, you can use our script in https://github.com/RaphaelKhoury/automated-apk-tracing
DOI: 10.5281/zenodo.6464626
title: Signal Analysis

description: Experimental Analysis.
DOI: 10.5281/zenodo.4678216
title: Signal Analysis

description: Experimental Analysis.
DOI: 10.5281/zenodo.6465282
title: Ictinia mississippiensis decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465281
title: Ictinia mississippiensis decontaminated FSCR

description: .
DOI: 10.3886/e138741v1-115200
title: Data and code for: The intergenerational effects of a large wealth shock: White Southerners after the Civil War

description: The nullification of slave wealth after the U.S. Civil War (1861-65) was one of the largest episodes of wealth compression in history. We document that white Southern households holding more slave assets in 1860 lost substantially more wealth by 1870, relative to Southern households that had been equally wealthy before the war. Yet, their sons almost entirely recovered from this wealth shock by 1900, and their grandsons completely converged by 1940. Marriage networks and connections to other elite families may have aided in recovery, whereas transmission of entrepreneurship and skills appear less central.
DOI: 10.5281/zenodo.6465287
title: Leptodoras nelsoni decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465286
title: Leptodoras nelsoni decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6465342
title: Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019

description: This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.
description: Column definitions location - recording location Extent_meters - Radial distance in meters (spatial extent) of non-crop vegetation measure VBRICH - Vocalizing bird richness Noncrop_perc - Noncrop vegetation percent within spatial extent Noncrop_m2 - Noncrop vegetation square meters within spatial extent Texture_Dis - Noncrop vegetation dissimilarity texture scaled to between 0 and 1 (unitless) Texture_Var -Noncrop vegetation variance texture scaled to between 0 and 1 (unitless) Pesticide - Whether pesticide was applied in 2019 during or prior to June in field adjacent to recorder. Binary measure of 1 or 0, 1 for yes, 0 for no. Fertilizer - Whether fertilizer was applied in 2019 during or prior to June in field adjacent to recorder. Binary measure of 1 or 0, 1 for yes, 0 for no.
DOI: 10.5281/zenodo.6465341
title: Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019

description: This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.
description: Column definitions location - recording location Extent_meters - Radial distance in meters (spatial extent) of non-crop vegetation measure VBRICH - Vocalizing bird richness Noncrop_perc - Noncrop vegetation percent within spatial extent Noncrop_m2 - Noncrop vegetation square meters within spatial extent Texture_Dis - Noncrop vegetation dissimilarity texture scaled to between 0 and 1 (unitless) Texture_Var -Noncrop vegetation variance texture scaled to between 0 and 1 (unitless) Pesticide - Whether pesticide was applied in 2019 during or prior to June in field adjacent to recorder. Binary measure of 1 or 0, 1 for yes, 0 for no. Fertilizer - Whether fertilizer was applied in 2019 during or prior to June in field adjacent to recorder. Binary measure of 1 or 0, 1 for yes, 0 for no.