cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4527035
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2016 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2016. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.6084/m9.figshare.13849331.v1
title: Chengdu PM2.5

description: Surface PM2.5 concentration in Chengdu
DOI: 10.6084/m9.figshare.13849331
title: Chengdu PM2.5

description: Surface PM2.5 concentration in Chengdu
DOI: 10.6084/m9.figshare.13849349
title: Chengdu radiation

description: surface radiation in Chengdu
DOI: 10.6084/m9.figshare.13849349.v1
title: Chengdu radiation

description: surface radiation in Chengdu
DOI: 10.5281/zenodo.4527044
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2018 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2018. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527043
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2018 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2018. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.6084/m9.figshare.13849379
title: Chengdu wind speed

description: Surface wind speed in Chengdu
DOI: 10.6084/m9.figshare.13849379.v1
title: Chengdu wind speed

description: Surface wind speed in Chengdu
DOI: 10.6084/m9.figshare.13849406
title: P1 P2 P3 profiles

description: Vertical profiles in Chengdu
DOI: 10.6084/m9.figshare.13849406.v1
title: P1 P2 P3 profiles

description: Vertical profiles in Chengdu
DOI: 10.5281/zenodo.4527047
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2019 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2019. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527046
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2019 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2019. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527052
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2020 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2020. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527051
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2020 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2020. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527042
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2017 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2017. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4527041
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2017 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2017. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4468607
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2000 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2000. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4468606
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2000 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2000. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5063/qj7fqx
title: Frog incidence frequency and soundscape diversity data used to construct the case study in Luypaert et al. (2021)

description: This data was used to construct the case study in:ENTER FULL REFERENCE HERE ONCE PUBLISHED1. Frog incidence frequency dataThis data represents a subset of the data described in:Bueno, A. S., Masseli, G. S., Kaefer, I. L., & Peres, C. A. (2020). Sampling design may obscure species–area relationships in landscape‐scale field studies.Ecography, 43(1), 107-118.The data consists of a subset of 105 sites on 62 forest islands and 4 continuous forest sites. The data was obtained for the project Ecological Impacts of River Damming on Forest Bird Assemblages in the Brazilian Amazon. Find out more at https://www.rufford.org/projects/anderson_saldanha_bueno.Raw data files corresponding to these frog observations are available for inspection on the ARBIMON platform:https://arbimon.rfcx.org/project/soundscape-frogdiv-relationship2. Acoustic index dataFor the purposes of the case study described in Luypaert et al. (2021), the oscillation index was used. The oscillation index was computed using a sampling rate of 41,000 Hz and a window size of 256. This metric quantifies the number of pulses, or amplitude modulations per second, and acoustic feature typical of insect and frog vocalizations. Indices were computed in the R programming language using the 'index_calc' function in the soundscapeR package (https://github.com/ThomasLuypaertGitHub/soundscapeR), which in turn calls on the QUT Ecoacoustics Analysis Programs software for its index computations (https://ap.qut.ecoacoustics.info/tutorials/01-usingap/practical?tabs=windows).
DOI: 10.5281/zenodo.4529075
title: Dataset for 'Meta-Learning Baselines and Database for Few-shot Classification in Agriculture'

description: Dataset for 'Meta-Learning Baselines and Database for Few-shot Classification in Agriculture'
DOI: 10.20375/0000-000d-fa82-6
title: Protest_Cooptation_Russia_Data-Documentation.pdf
DOI: 10.20375/0000-000d-fa83-5
title: Protest_Cooptation_Russia.dta
DOI: 10.48320/0d12074f-bd3b-4d28-a3b7-58542f665e97
title: Protest mobilization and cooptation of parliamentary opposition parties in Russia's regions
DOI: 10.5281/zenodo.4527829
title: India Non Virus Deaths During lockdown

description: This is a public database of reported deaths that have happened as a result of the lockdown. These include deaths due to starvation and financial distress, exhaustion, accidents during migration, lack or denial of medical care, suicides, police brutality, crimes, and alcohol-withdrawal. We also have a few deaths wherein more details are needed, or where conflicting reports emerge. We put such deaths in the ‘unclassified’ category. More details.
description: Website : https://thejeshgn.com/projects/covid19-india/non-virus-deaths/
DOI: 10.5281/zenodo.4527601
title: Why do Builds Fail? – A Conceptual Replication Study

description: This package is our dataset of a replication study of Rausch et al. and Zolfagharina et al. to explore features relation with Build Failure. The Rausch et al. paper: An empiricalanalysis of build failures in the continuous integration work-flows of java-based open-source software The Zolfagharina et al. paper: Do Not Trust Build Results at Face Value – An Empirical Study of 30 Million CPAN Builds We replicate 7 features dimension from Rausch et al. and 1 dimension from Zolfagharina et al. and we added also 2 features dimensions about code quality using code and test smells. Paper Link: https://mcis.cs.queensu.ca/publications/2021/jss_amine.pdf