AndreaBozzo/ceres-open-data-index huggingface dataset:
Wind Integration National Dataset (WIND) Toolkit - Multi-year Annual Average - Canada

This data provides modeled annual average wind speed for Southern Canada both onshore and offshore for the period 2007–2013.

This dataset was derived from the WIND Toolkit and may be used with the following citations:

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.

Lieberman-Cribbin, W., C. Draxl, and A. Clifton. 2014. Guide to Using the WIND Toolkit Validation Code (Technical Report, NREL/TP-5000-62595). Golden, CO: National Renewable Energy Laboratory.

King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.
Wind Integration National Dataset (WIND) Toolkit - Multi-year Annual Average - Mexico

This data provides modeled annual average wind speed for Mexico and surrounding areas both onshore and offshore for the period 2007–2013.

This dataset was derived from the WIND Toolkit and may be used with the following citations:

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.

Lieberman-Cribbin, W., C. Draxl, and A. Clifton. 2014. Guide to Using the WIND Toolkit Validation Code (Technical Report, NREL/TP-5000-62595). Golden, CO: National Renewable Energy Laboratory.

King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.
Wind Integration National Dataset (WIND) Toolkit - Multi-year Annual Average - United States

This data provides modeled annual average wind speed for the contiguous United States both onshore and offshore for the period 2007–2013.

This dataset was derived from the WIND Toolkit and may be used with the following citations:

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.

Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.

Lieberman-Cribbin, W., C. Draxl, and A. Clifton. 2014. Guide to Using the WIND Toolkit Validation Code (Technical Report, NREL/TP-5000-62595). Golden, CO: National Renewable Energy Laboratory.

King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.
Developing Geothermal Plants

This dataset contains locations of geothermal power plants in development within the United States as of the publication date and includes attributes for planned capacity. Geothermal developing plant data was aggregated from SNL Financial LC, the Geothermal Energy Association (GEA), press releases, operator websites and geothermal lease data. NREL performed independent research to validate locations of geothermal projects under development as of July 2014.
Hydrogen Potential from Renewable Energy Resources (2007)

This study was conducted in 2007 to estimate the potential for producing hydrogen from key renewable resources (onshore wind, solar photovoltaic, and biomass) by county in the United States and to create maps that allow the reader to easily visualize the results. To accomplish this objective, the authors analyzed renewable resource data both statistically and graphically utilizing a state-of-the-art Geographic Information System (GIS), a computer-based information system used to create and visualize geographic information. Land-use and environmental exclusions were applied to represent the most viable resources across the country. While wind, solar, and biomass are considered major renewable resources, other renewable energy resources could also be used for hydrogen production, thus contributing to hydrogen development locally and regionally. These additional resources include offshore wind, concentrating solar power, geothermal, hydropower, photoelectrochemical, and photobiological resources. This study found that approximately 1 billion metric tons of hydrogen could be produced annually from wind, solar, and biomass resources in the United States. The greatest potential for producing hydrogen from these key renewable resources is in the Great Plains region. In addition, this research suggests that renewable hydrogen has the potential to displace gasoline consumption in most states if and when a number of technical and scientific barriers can be overcome.
Wave Depth

The project estimated the naturally available and technically recoverable U.S. wave energy resources, using a 51-month Wavewatch III hindcast database developed especially for this study by National Oceanographic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction. For total resource estimation, wave power density in terms of kilowatts per meter is aggregated across a unit diameter circle. This approach is fully consistent with accepted global practice and includes the resource made available by the lateral transfer of wave energy along wave crests, which enables densities within a few kilometers of a linear array, even for fixed terminator devices.
Wave Energy Period

The project estimated the naturally available and technically recoverable U.S. wave energy resources, using a 51-month Wavewatch III hindcast database developed especially for this study by National Oceanographic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction. For total resource estimation, wave power density in terms of kilowatts per meter is aggregated across a unit diameter circle. This approach is fully consistent with accepted global practice and includes the resource made available by the lateral transfer of wave energy along wave crests, which enables densities within a few kilometers of a linear array, even for fixed terminator devices.
Wave Significant Height

The project estimated the naturally available and technically recoverable U.S. wave energy resources, using a 51-month Wavewatch III hindcast database developed especially for this study by National Oceanographic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction. For total resource estimation, wave power density in terms of kilowatts per meter is aggregated across a unit diameter circle. This approach is fully consistent with accepted global practice and includes the resource made available by the lateral transfer of wave energy along wave crests, which enables densities within a few kilometers of a linear array, even for fixed terminator devices.
Wave Hindcast

The project estimated the naturally available and technically recoverable U.S. wave energy resources, using a 51-month Wavewatch III hindcast database developed especially for this study by National Oceanographic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction. For total resource estimation, wave power density in terms of kilowatts per meter is aggregated across a unit diameter circle. This approach is fully consistent with accepted global practice and includes the resource made available by the lateral transfer of wave energy along wave crests, which enables densities within a few kilometers of a linear array, even for fixed terminator devices.
Wave Power Density

The project estimated the naturally available and technically recoverable U.S. wave energy resources, using a 51-month Wavewatch III hindcast database developed especially for this study by National Oceanographic and Atmospheric Administration's (NOAA's) National Centers for Environmental Prediction. For total resource estimation, wave power density in terms of kilowatts per meter is aggregated across a unit diameter circle. This approach is fully consistent with accepted global practice and includes the resource made available by the lateral transfer of wave energy along wave crests, which enables densities within a few kilometers of a linear array, even for fixed terminator devices.
National Solar Radiation Database (NSRDB)

The NSRDB is a serially complete collection of hourly and half-hourly values of the three most common measurements of solar radiation—global horizontal, direct normal, and diffuse horizontal irradiance—and meteorological data. The current NSRDB is modeled using multi-channel measurements from geostationary satellites. The older versions of the NSRDB were modeled using cloud and weather information primarily collected at airports. A sufficient number of locations and temporal and spatial scales were used to represent regional solar radiation climates accurately.
Physical Solar Model version 3 Direct Normal Irradiance Multi-year Monthly Average

This data provides monthly average daily total solar resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate direct normal irradiance (DNI) and global horizontal irradiance (GHI). The DNI and GHI are computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, the FARMS is used to compute the DNI. The DNI for cloud scenes is then computed using the DISC model. The data are averaged from hourly model output over 19 years (1998-2016). The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, daily snow cover data from the NSIDC and mixing ratio, temperature and pressure profiles from the Modern Era-Retrospective Analysis (MERRA-2) dataset. The REST2 model uses hourly aerosol optical depth from MERRA-2 to calculate GHI and DNI; water vapor and other inputs for REST 2 are obtained from the MERRA-2. 

This dataset was derived from the NSRDB and may be used with the following citation:

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 89, 51-60.
On the microstructure evolution of AA6061 with pulsed laser powder bed fusion

"This study uncovers new opportunities for microstructure engineering of AA6061 using pulsed lasers in powder bed fusion. Experimental analysis reveals that pulsed lasers allow for extensive control of grain sizes. Lower frequencies or duty cycles induce remelting cycles that promote finer grains and homogenized subgrain structures, while higher frequencies with high duty cycles sustain elevated temperatures, reducing cooling rates and resulting in coarser grains. Pulsing strategy is demonstrated as facile route for tailoring microstructure through enhanced control over solidification. These findings present an effective means in additive manufacturing by linking pulsed laser dynamics to microstructure evolution towards desired material properties.

This study unveils effective grain structure control using pulsed lasers without alloy modification, achieving texture homogenization and uniform subgrain structure through multiple melt pool boundaries."
Physical Solar Model version 3 Direct Normal Irradiance Multi-year Monthly Average

This data provides monthly average daily total solar resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate direct normal irradiance (DNI) and global horizontal irradiance (GHI). The DNI and GHI are computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, the FARMS is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data are averaged from hourly model output over 19 years (1998-2016). The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, daily snow cover data from the NSIDC and mixing ratio, temperature and pressure profiles from the Modern Era-Retrospective Analysis (MERRA-2) dataset. The REST2 model uses hourly aerosol optical depth from MERRA-2 to calculate GHI and DNI; water vapor and other inputs for REST 2 are obtained from the MERRA-2.

This dataset was derived from the NSRDB and may be used with the following citation:

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 89, 51-60.
Physical Solar Model version 3 Global Horizontal Irradiance Multi-year Annual Average

This data provides annual average daily total solar resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate direct normal irradiance (DNI) and global horizontal irradiance (GHI). The DNI and GHI are computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, the FARMS is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data are averaged from hourly model output over 19 years (1998-2016). The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, daily snow cover data from the NSIDC and mixing ratio, temperature and pressure profiles from the Modern Era-Retrospective Analysis (MERRA-2) dataset. The REST2 model uses hourly aerosol optical depth from MERRA-2 to calculate GHI and DNI; water vapor and other inputs for REST 2 are obtained from the MERRA-2.

This dataset was derived from the NSRDB and may be used with the following citation:

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 89, 51-60.
Physical Solar Model version 3 Global Horizontal Irradiance Multi-year Monthly Average

This data provides monthly average daily total solar resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate direct normal irradiance (DNI) and global horizontal irradiance (GHI). The DNI and GHI are computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, the FARMS is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data are averaged from hourly model output over 19 years (1998-2016). The PATMOS-X model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, daily snow cover data from the NSIDC and mixing ratio, temperature and pressure profiles from the Modern Era-Retrospective Analysis (MERRA-2) dataset. The REST2 model uses hourly aerosol optical depth from MERRA-2 to calculate GHI and DNI; water vapor and other inputs for REST 2 are obtained from the MERRA-2.

This dataset was derived from the NSRDB and may be used with the following citation:

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews, 89, 51-60.
Office of Nuclear Energy website

The Office of Nuclear Energy's (NE) primary mission is to advance nuclear power as a resource capable of making major contributions in meeting our Nation’s energy supply, environmental, and energy security needs. We seek to resolve technical, cost, safety, security and regulatory issues through research, development and demonstration. By focusing on the development of advanced nuclear technologies, NE supports the Administration’s goals of providing domestic sources of secure energy, reducing greenhouse gases, and enhancing national security.

NE’s program is guided by the four research objectives detailed in its Nuclear Energy Research and Development Roadmap:

Develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the life of current reactors. 
Develop improvements in the affordability of new reactors to enable nuclear energy to help meet the Administration’s energy security and climate change goals. 
Develop sustainable fuel cycles. 
Understand and minimize the risks of nuclear proliferation and terrorism. 
NE serves present and future U.S. energy needs by developing critical technologies for the future and helping to train tomorrow’s workforce. The benefits of nuclear power as a safe, carbon-free, reliable and secure source of energy make it an essential element in our Nation’s energy and environmental future.
Nuclear Energy Webpage

The Office of Nuclear Energy’s (NE) primary mission is to advance nuclear power as a resource capable of making major contributions in meeting our Nation’s energy supply, environmental, and energy security needs. We seek to resolve technical, cost, safety, security and regulatory issues through research, development and demonstration. By focusing on the development of advanced nuclear technologies, NE supports the Administration’s goals of providing domestic sources of secure energy, reducing greenhouse gases, and enhancing national security.

NE’s program is guided by the four research objectives detailed in its Nuclear Energy Research and Development Roadmap:
•Develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the life of current reactors.
•Develop improvements in the affordability of new reactors to enable nuclear energy to help meet the Administration’s energy security and climate change goals.
•Develop sustainable fuel cycles.
•Understand and minimize the risks of nuclear proliferation and terrorism.

NE serves present and future U.S. energy needs by developing critical technologies for the future and helping to train tomorrow’s workforce. The benefits of nuclear power as a safe, carbon-free, reliable and secure source of energy make it an essential element in our Nation’s energy and environmental future.
Office of Electricity website

The Office of Electricity (OE) provides national leadership to ensure that the Nation’s energy delivery system is secure, resilient and reliable. OE works to develop new technologies to improve the infrastructure that brings electricity into our homes, offices, and factories, and the federal and state electricity policies and programs that shape electricity system planning and market operations. OE also works to bolster the resiliency of the electric grid and assists with restoration when major energy supply interruptions occur.
GC-MS Surface Response Data for Instrument Optimization

The objective of this example case was to maximize peak height and minimize peak width of 11 iodinated standards analyzed using gas chromatography-mass spectrometry (GC-MS). Once the significant (or most significant) parameters have been determined in a prior screening experiment, another set of experiments must be designed in which a response surface objective experiment is performed to collect data for optimization. In this example case, surface response experiments were performed using a 3-level Box-Behnken design in which each statistically significant parameter (e.g. split ratio, carrier gas flow rate, oven temperature ramp rate, and capillary column film thickness) was assigned a “low”, “middle”, and “high” value. For these experiments, the three nonsignificant parameters, inlet temperature, injection volume, and auxiliary line temperature, were fixed at 200 °C, 2 μL, and 280 °C, respectively. The experiments were conducted in the same manner as described in the screening experiments section. The attached CSV files contain the Box-Behnken design (bbd.csv), the raw chromatographic data for the 25 distinct methods (Method #.csv), and the retention times (bb_rts.csv) for each iodinated standard.
GC-MS Screening Data for Instrument Optimization

The objective of this example case was to maximize peak height and minimize peak width of 11 iodinated standards analyzed using gas chromatography-mass spectrometry (GC-MS). Seven parameters (split ratio, carrier gas flow rate, inlet temperature, injection volume, auxiliary line temperature, oven temperature ramp rate, and capillary column film thickness) were chosen due to their known impact on GC−MS performance metrics (i.e., minimized peak width and maximized peak height) optimized in this study. To limit the number of experiments required for the surface response experiments, screening experiments were performed to determine the statistically significant parameters. A two-level fractional factorial design was created with 32 distinct observations (methods) with a resolution of 4 and a defining aliasing pattern of ABCF = CDEFG = I. The data was collected using an Agilent GC-MS (7890 GC, 7010C mass spectrometer, Agilent Technologies, Santa Clara, CA) operated in full scan mode. Chromatographic separation was performed using a Restek Rtx-1701 GC column (30 m × 0.25 mm ID; Restek Corporation, Bellefonte, PA). The source temperature was held at 230 °C and the quadrupoles were held at 150 °C. An emission current of 100 μA and ionization energy of 70 eV were used. The oven temperature program was held at 35 °C for 5 minutes and then increased at the chosen oven temperature ramp rate to 280 °C and held for 15 minutes. The attached CSV files contain the fractional factorial design (ffd.csv), the raw chromatographic data for the 32 distinct methods (TIC_Method_#.csv), and the retention times (rts.csv) for each iodinated standard.