Find research datasets worth reusing

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-I

This data file contains data for changes in atmospheric heating due to changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-II

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-III

This data file contains data for the pan-arctic vegetation map depicted in Figure 1 of Euskirchen et al (2007). See Euskirchen et al. (2007) for further details on the construction of this map.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-IV

This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-V

This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1970-2000. See Euskirchen et al. (2007) for full study details.

opencc-by-4.0Dec 2019View details →

Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"

These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions. The data are used in the manuscript entitled "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model".

dryad36/100

Aligning renewable energy expansion with climate-driven range shifts

Fossil fuel dependence can be reduced, in part, by renewable energy (RE) expansion. Increasingly, RE siting seeks to avoid significant impacts on biodiversity but rarely considers how species ranges will shift under climate change. Here, we undertake a systematic literature review on the topic and overlay future RE siting maps with the ranges of two threatened species under future climate scenarios to highlight this potential conflict.

opencc-zeroFeb 2024View details →

opencc-by-4.0Nov 2024View details →

Climate Energy Dataset For Off Grid Electricity Infrastructure

The dataset comprises real-time electrical measurements—including voltages, currents, and power factors—for three-phase and single-phase systems across generation, distribution, and consumption stages showcasing the energy generation and demand within an off-grid electricity infrastructure located in the Kalam Region, a specific climate zone in Pakistan. Collected every five minutes from March 6, 2023, to October 24, 2024, it includes over 45 million instances covering data from four micro-hydropower generators, 26 transformers, and 585 end-users. Additionally, the dataset incorporates climate data—such as temperature, dew point, wind components, precipitation, snowfall, and snow cover—from the ERA5 dataset. This comprehensive collection enhances its utility for research in energy systems analysis, climate change studies, electrical engineering, and artificial intelligence applications. This dataset was originally created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information.

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).