AndreaBozzo/ceres-open-data-index huggingface dataset:
Genome-wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a high-throughput application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a thermophile. We applied DAP-seq to &gt;90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA- and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike towards a desired phenotype.

Tags: bioinformatics, clostridium-thermocellum, dap-seq, dna-binding-site, regulon, transcription-factor, transcriptional-regulatory-networks
Organization: Department of Energy
Long-run Marginal CO2 Emission Rates Workbooks for 2020 Standard Scenarios Cambium Data

This dataset has been superseded by a new set of workbooks that can be found here: https://data.nlr.gov/submissions/183&nbsp;These workbooks contain modeled estimates of long-run marginal CO2 emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed.&nbsp;In addition to year-over-year data, the Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand.&nbsp;Three future scenarios are provided in separate workbooks: A Mid-case (i.e., business-as-usual), and two scenarios with relatively higher or lower renewable energy costs. For more details on these scenarios, see the Standard Scenarios 2020 Report (https://www.nlr.gov/docs/fy21osti/77442.pdf). For more data underlying each scenario, see the Standard Scenarios 2020 project (Cambium data) at https://cambium.nlr.gov/.&nbsp;This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2020 (https://www.nlr.gov/docs/fy21osti/78239.pdf).&nbsp;This data is planned to be updated annually. Information on the latest versions can be found at https://www.nlr.gov/analysis/cambium.html.

Tags: cambium, co2, greenhouse-gas-emissions, long-run, marginal-emissions, nlr, plexos, reeds
Organization: Department of Energy
Solar Permitting, Inspection, and Interconnection Cycletimes and Requirements

Twenty three small-to-large residential, commercial, and industrial PV installers submitted project-level data for analysis, which included timestamps for major components of the permitting, inspection, and interconnection process as well as contract and installation dates. NREL calculated median timelines for these processes across the authorities having jurisdiction (AHJs) and utilities represented in the data. Also included are relevant permitting requirements for AHJs and interconnection process requirements for utilities. This dataset represents the underlying results shown in the Solar Time-based Residential Analytics and Cycle time Estimator (SolarTRACE) viewer. This dataset has been reviewed but errors may exist, and it may not be comprehensive. Errors in the sources (e.g., AHJ permitting requirements lists from data partners) may be duplicated in the dataset.

Tags: distributed-solar-pv, distributed-storage, residential-solar, residential-storage, rooftop-solar, solar-permitting
Organization: Department of Energy
Distributed PV Permitting Survey Responses

In 2019, NREL, in partnership with the Solar Energy Industries Association, developed a survey for PV installers on their experiences with delays and cancelations in the adoption process, including the impacts of permitting and interconnection. This dataset includes the anonymized responses from 147 representatives from 136 solar install companies.

Tags: distributed-solar-pv, residential-solar, rooftop-solar
Organization: Department of Energy
U.S. Solar Photovoltaic BESS System Cost Benchmark Q1 2020 Report

NREL has been modeling U.S. solar photovoltaic (PV) system costs since 2009. This year, our report benchmarks costs of U.S. PV for residential, commercial, and utility-scale systems, with and without storage, built in the first quarter of 2020 (Q1 2020). The methodology includes bottom-up accounting for all system and project-development costs incurred when installing residential, commercial, and utility-scale systems and it models the capital costs and levelized cost of energy for such systems.

Tags: 2020, capital-cost, lcoe, lcoss, levelized-cost-of-energy, overnight-capital-cost, pv-photovoltaic-solar-energy
Organization: Department of Energy
Electrification Futures Study Supply-Side Scenarios Report Figure Data

The file includes the data for figures in the Electrification Futures Study Supply-Side Scenarios report. Additional information on the data can be found in the report: Murphy, Caitlin, Trieu Mai, Yinong Sun, Paige Jadun, Matteo Muratori, Brent Nelson, and Ryan Jones. 2021. Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-72330. https://www.nlr.gov/docs/fy21osti/72330.pdf.

Tags: efs, electrification-futures-study, power-system, scenario-data
Organization: Department of Energy
TMY3 Weather Data for ComStock and ResStock

A set of TMY3 EPW weather files for each county of the U.S.&nbsp;NOTE: A file was uploaded on 2022/10/05 with additions to address known missing data for Nolan County, TX (FIPS: 48353), Fisher County, TX (FIPS: 48151), and Stonewall County, TX (FIPS: 48433).

Tags: weather
Organization: Department of Energy
Grid Service Values of Generic Marginal Building Flexibility in Modeled 2030 U.S. Power Systems

The datasets include the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of generic, daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. The results should be interpreted along with the caveats listed in "Valuation of Building Flexibility: Grid Service Value for Initial Market Entrants in Projected 2030 United States Power Systems", which also documents the methods used for the study.&nbsp;The factors examined in the study include: grid scenario, region, original usage hour in the day (local time), and building flexibility parameters - efficiency, dissipation, and shifting window include max pre-shift and max post-shift.&nbsp;Filenames in the datasets:&nbsp;The filenames contain information on the grid scenario, and the building flexibility efficiency and dissipation. For example: MidCase_2030_efficiency1.25_dissipation0.05_value.csv contains all results under Mid RE 2020, efficiency = 1.25, and dissipation = 0.05.&nbsp;* MidCase = Mid RE&nbsp;Each .csv file contains:&nbsp;region: The location of the building flexibility, aligned with U.S. Energy Information Administration (EIA) National Energy Modeling System (NEMS) Electricity Market Module regions.&nbsp;max_pre_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift earlier.&nbsp;max_post_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift later.&nbsp;local_datetime: Original datetime of 1kWh of building energy consumption.&nbsp;local_orig_h: Original usage hour (1-24) of building energy consumption, ignores daylight saving.&nbsp;local_shift_to: The datetime to when building energy consumption shifts, if shifting happens. If no shifting occurs, this cell is left blank.&nbsp;energy: Net energy value of energy shifting.&nbsp;capacity: Net capacity value of energy shifting.&nbsp;shifting_value: Net energy plus capacity value of energy shifting.&nbsp;spin: Spin reserve value at the original datetime of consumption.&nbsp;flex: Flexible reserve value at the original datetime of consumption.&nbsp;reg: Regulation reserve value at the original datetime of consumption.&nbsp;total_profit: Assuming the building flexibility is capable of providing energy, capacity, and ancillary services, total profit is the maximum value of energy shifting value, spin reserve value, flexible reserve value, and regulation reserve value - one of the four choices at any given hour - because we do not allow its value to be double-counted.

Tags: building-flexibility, demand-response, grid-integration, grid-interactive-efficient-buildings
Organization: Department of Energy
PV Inverter Experimental Dataset Version 2 with 100 Percent Power

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 20 kW off-the-shelf grid following PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. The PV inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.88 to 1.09 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59.4 Hz to 60.45 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

Tags: artificial-intelligence, data-analysis, data-visualizations, database-for-artificial-intelligence-training, database-for-inverter-characterization, database-for-model-validation, machine-learning, pv-inverter, pytorch, reinforcement-learning, scikit-learn, tensorflow
Organization: Department of Energy
Caerbannog Point Clouds

The Caerbannog Point Clouds provide point-sampled 3D models occluded in clouds of points. We synthesized the 3D point clouds from polygonal models, point-sampling the models and surrounding them in a point cloud such that the shape of the model is occluded in any 2D projection. We obscure our model-of-interest by repeatedly surrounding it with an amorphous cloud of points, giving the overall point cloud a structure of organic nature, like that of shrubbery. In a user study, participants were significantly better at identifying the models when visualized as 3D scatterplots under rotation than in axis-aligned 2D scatterplots. We provide three point-clouds, both occluded and unoccluded: the Stanford bunny, Utah Teapot, and OSG Cow.

Tags: data-analysis, data-visualizations, scatterplots, visual-perception
Organization: Department of Energy
U.S. Voluntary Green Power Market Data 2019

This data book provides certain data behind figures and tables found in the NREL presentation, Status and Trends in the U.S. Voluntary Green Power Market (2019 Data). These data reflect estimates based on the best available data.

Tags: biomass, energy-certificates, geothermal, green-power-sales, hydropower, market, recs, solar, voluntary, wind
Organization: Department of Energy
2010 Census Tracts

Census Tracts from the 2010 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.

All previously released versions of this data are available on the <a href="https://www.nyc.gov/content/planning/pages/resources/datasets/census-tracts">DCP Website: BYTES of the BIG APPLE</a>. Current version: 26a

Tags: 2010, boundaries, boundary, census-tract, cscl, dcp, department-of-city-planning, districts, lion, new-york-city, nyc, us-census-bureau
Organization: City of New York
Levelized Cost of Charging Electric Vehicles

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs).&nbsp;The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year.&nbsp;Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.

Tags: charging, cost-of-electricty, demand-charge, electric-vehicle, fuel-cost, time-of-use, utility-rate-analysis
Organization: Department of Energy
DEPRECATED - Sharing the Sun Community Solar Project Data (June 2020)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/244&nbsp;This database represents a list of community solar projects identified through various sources as of June 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects.&nbsp;Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Tags: community-solar, solar, utility-service-territory
Organization: Department of Energy
2020 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2020 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. The Transportation ATB includes current and projected estimates through 2050 for light-duty vehicle technologies as well as conventional and alternative fuels.&nbsp;This excel files include vehicle, fuel, and joined vehicle and fuel data with calculated levelized cost and emission values associated with the 2020 Transportation ATB. NREL has also provided a Tableau workbook to further explore the data.&nbsp;A website documents this data at https://atb.nrel.gov.

Tags: battery-electric-vehicle, biofuels, cost-and-performance, electricity, fuel-cell-electric-vehicle, fuel-economy, hybrid-electric-vehicle, hydrogen, internal-combustion-engine-vehicle, lcod, levelized-cost-of-driving, light-duty-vehicle, plug-in-hybrid-electric-vehicle, projection, transportation, united-states, vehicle-cost
Organization: Department of Energy
Deep Green Unannotated Protein Structures

The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology.&nbsp;References:&nbsp;Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589.&nbsp;Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

Tags: alphafold, arabidopsis-thaliana, chlamydomonas-reinhardtii, donald-danforth-plant-science-center, energy-crop, green-lineage, model-species, protein-structure, setaria-viridis, unannotated-proteins
Organization: Department of Energy
Fire Companies

Fire Companies boundaries of New York City.

All previously released versions of this data are available on the <a href="https://www.nyc.gov/content/planning/pages/resources/datasets/fire-battalions-companies-divisions">DCP Website: BYTES of the BIG APPLE</a>. Current version: 26a

Tags: boundaries, boundary, cscl, dcp, department-of-city-planning, districts, fdny, fire-companies, lion, new-york-city, nyc
Organization: City of New York
Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies

This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system contingency analyses along with graph and topology measurements under each contingency scenario of the power system.

Tags: graph-theory, machine-learning, power-system, resilience, simulation, topological-data-analysis
Organization: Department of Energy
Roadbed Pointer List (RPL)

A text file representing the relationships between generic and roadbed segments (or roadbed terminators segments that represent the merging of divided roadbeds back into each other as a single roadbed or undivided street) by segment ID.

All previously released versions of this data are available on the <a href="https://www.nyc.gov/content/planning/pages/resources/datasets/road-pointer-list">DCP Website: BYTES of the BIG APPLE</a>. Current version: 26a

Organization: City of New York
2020 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This updated version of the workbook provides data for the 2020 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. A website documents this data at https://atb.nrel.gov.

Tags: battery-energy-storage, capacity-factor, capex, capital-expenditures, concentrating-solar-power, cost-and-performance, geothermal, hydropower, lcoe, levelized-cost-of-energy, operations-and-maintenance, overnight-capital-cost, projection, pv-photovoltaic-solar-energy, renewable-energy, united-states, wind
Organization: Department of Energy
Puerto Rico Solar-for-All: LMI PV Rooftop Technical Potential and Solar Savings Potential

The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level. Each dataset is broken out by income group, defined by the Area Median Income (AMI), by tenure, and by building type. The underlying LiDAR data used the estimation were collected in 2015-2017 and the American Community Survey (ACS) data are from 2011-2015. This Puerto Rico Solar-For-All dataset is intended to extend the SEEDSII Solar-For-All estimates for the US 50-states (https://www.nlr.gov/docs/fy18osti/70901.pdf) for the territory of Puerto Rico using updated methods. Please see the data documentation for details.

Tags: lmi, low-and-moderate-income, low-income, photovoltaics, pr, puerto-rico, pv, residential, rooftop, solar, solar-energy, solar-for-all, tract
Organization: Department of Energy
2020 Census Blocks (water areas included)

Census Blocks from the 2020 US Census for New York City including portions under water. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map.

All previously released versions of this data are available on the <a href="https://www.nyc.gov/content/planning/pages/resources/datasets/census-blocks">DCP Website: BYTES of the BIG APPLE</a>. Current version: 26a

Tags: 2020, boundaries, boundary, census-block, cscl, dcp, department-of-city-planning, districts, lion, new-york-city, nyc, us-census-bureau
Organization: City of New York
Preliminary Mayor's Management Report Spending and Budget

Contains Spending and Budget information for City agencies, as reported in the Preliminary Mayoral Management Report (PMMR).

Tags: adopted-budget, budget, expenditures, expenses, key-performance-indicators, kpi, kpis, mayors-management-report, office-of-management-and-budget, omb, other-than-personal-services, otps, personal-services, pmmr, pmmr-additional-tables, preliminary-mayors-management-report, spending, spending-and-budget, spending-and-budget-table, units-of-appropriation
Organization: City of New York