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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NOAA-GFDL.GFDL-ESM4.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The GFDL-ESM4 climate model, released in 2018, includes the following components: aerosol: interactive, atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa), atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry), land: GFDL-LM4.1, landIce: GFDL-LM4.1, ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-COBALTv2, seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories). The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

For this manuscript, there were three types of methods performed to make our main conclusions: genetic diversity and structure analyses, downloading and mapping butternut fossil pollen during the last 20,000 years, and modeling and hindcasting butternut's (Juglans cinerea) distribution 20,000 years ago to present. Genetic analyses and species distribution modeling were performed in Emily Schumacher’s Github repository (https://github.com/ekschumacher/butternut) and pollen analyses and mapping were performed in Alissa Brown’s repository (https://github.com/alissab/juglans). Here is information detailing the Genetic data Data collection description: To perform genetic diversity and structure analyses on butternut, we used genetic data from the publication Hoban et al. (2010) and genetic data from newer sampling efforts on butternut from 2011 - 2015. Individuals were collected by Jeanne Romero-Severson, Sean Hoban, and Martin Williams over the course of ~ten years with a major sampling effort closer to 2009 followed up by another round of sampling 2012 - 2015. The initial 1,004 butternut individuals that were collected were genotyped by Sean Hoban and then the subsequent 757 individuals were genotyped in the Romero-Severson lab at Notre Dame non-consecutively. Genotyping was performed according to Hoban et al. (2008); DNA was extracted from fresh cut twigs using DNeasy Plant Mini kits (QIAGEN). PCR was performed by using 1.5 mM MgCl2, 1x PCR buffer [50 mm KCl, 10 mm Tris-HCl (pH 9.0), 0.1% Triton-X-100 (Fisher BioTech)], 0.2 mm dNTPs, 4 pm each forward and reverse primer, 4% Bovine Serum Albumin, 0.25 U TaKaRa Ex Taq Polymerase (Panvera), and 20 ng DNA template (10 μL total volume). The PCR temperature profile was as follows: 2 min at 94 °C; 30 cycles of 94 °C for 30 s, Ta for 30 s, and 72 °C for 30 s; 45 min at 60 °C; and 10 min at 72 °C on a PTC-225 Peltier Thermal Cycler (MJ Research). The process of assessing loci and rebinning for differences in years is detailed in the Schumacher et al. (2022) manuscript. Data files butternut_44pop.gen: Genepop file of original 1,761 butternut individuals, sampling described above, separated into original 44 sampling populations. butternut_24pop_nomd.gen: Genepop file of 1,635 butternut individuals, following rebinning based on researcher binning, reduced based on geographic isolation and missing data, organized into 24 populations. Used to generate all genetic diversity results. butternut_24pop_relate_red.gen: Genepop file of 993 butternut individuals, reduced for 25% relatedness, used to generate all clustering analyses. butternut_26pop_nomd.gen: Genepop file of 1,662 butternut individuals, reduced based on geographic isolation and missing data, including Quebec individuals, organized into 26 populations. Used to generate genetic diversity results with Quebec individuals. butternut_26pop_relate_red.gen: Genepop file of 1,015 butternut individuals, including Quebec individuals, reduced for 25% relatedness, used to generate clustering analyses with Quebec individuals. Fossil Pollen Data collection description: Pollen records for butternut were downloaded from Neotoma Paleoecology Database in 500-year time increments and visualized in 1,000 year-time increments 20,000 years ago to present. Data files butternut_pollen_data.csv: CSV of pollen records used for analyses and mapping. Includes original coordinates for each record (“og_long”, “og_lat”), the count of Juglans cinerea pollen at each site (“Juglans_cinerea_count”), and the age of the record (“Age”). To create the final maps, the coordinates were projected into Albers for each record (“Proj_Long,” “Proj_Lat”). Species Distribution Modeling and Hindcast Modeling Data collection description: We wanted to identify butternut's ecological preferences using boosted regression trees (BRT) and then hindcast distribution models into the past to identify migration pathways and locations of glacial refugia. Species distribution modeling was performed using boosted regression trees according to Elith et al. (2008). To run BRT, we needed to: 1. Reduce occurrence records to account for spatial autocorrelation, 2. Generate pseudo-absence points to identify the habitat where butternut is not found, 3. Obtain and extract the 19 bioclimatic variables at all points, 4. Select ecological variables least correlated with each other and most correlated with butternut presence. The BRT model that predicted butternut's ecological niche was then used to hypothesize butternut's suitable habitat and range shifts in the past. We downloaded occurrence records according to Beckman et al. (2019) as described here: https://github.com/MortonArb-ForestEcology/IMLS_CollectionsValue. The habitat suitability map generated from the BRT were projected into the past 20,000 years using Paleoclim variables (Brown et al., 2018). Data files butternut_BRT_var.csv: A CSV of the butternut presence and pseudoabsence points and extracted Bioclim variables (Fick & Hijman, 2017) used to run BRT in the final manuscript. Longitude and latitude coordinates are projected into Albers Equal Area Conic project, same with all of the ecological variables. Presence points are indicated with a 1 in the “PA” column and pseudo-absence points are indicated with a “0.” The variables most correlated with presence and least correlated with each other in this analysis were precipitation of the wettest month (“PwetM”), mean diurnal range (“MDR”), mean temperature of the driest quarter (“MTDQ”), mean temperature of the wettest quarter (“MTwetQ”), and seasonal precipitation (“precip_season”). References Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C., & Haywood, A. M. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Scientific Data, 5, 1-9 Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802-813. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302-4315. Hoban, S., Anderson, R., McCleary, T., Schlarbaum, S., and Romero-Severson, J. (2008). Thirteen nuclear microsatellite loci for butternut (Juglans cinerea L.). Molecular Ecology Resources, 8, 643-646. Hoban, S. M., Borkowski, D. S., Brosi, S. L., McCleary, T. S., Thompson, L. M., McLachlan, J. S., ... Romero-Severson, J. (2010). Range‐wide distribution of genetic diversity in the North American tree Juglans cinerea: A product of range shifts, not ecological marginality or recent population decline. Molecular Ecology, 19, 4876-4891. Aim: Range shifts are a key process that determine species distributions and genetic patterns. A previous investigation reported that Juglans cinerea (butternut) has lower genetic diversity at higher latitudes, hypothesized to be the result of range shifts following the last glacial period. However, genetic patterns can also be impacted by modern ecogeographic conditions. Therefore, we re-investigate genetic patterns of butternut with additional northern population sampling, hindcasted species distribution models, and fossil pollen records to clarify the impact of glaciation on butternut. Location: Eastern North America Taxon: Juglans cinerea (L., Juglandaceae) (butternut) Methods: Using 11 microsatellites, we examined range-wide spatial patterns of genetic diversity metrics (allelic richness, heterozygosity, FST) for previously studied butternut individuals and an additional 757 samples. We constructed hindcast species distribution models and mapped fossil pollen records to evaluate habitat suitability and evidence of species’ presence throughout space and time. Results: Contrary to previous work on butternut, we found that genetic diversity increased with distance to range edge, and previous latitudinal clines in diversity were likely due to a few outlier populations. Populations in New Brunswick, Canada were genetically distinct from other populations. At the Last Glacial Maximum, pollen records demonstrate butternut likely persisted near the glacial margin, and hindcast species distribution models identified suitable habitat in the southern United States and near Nova Scotia. Main conclusions: Genetic patterns in butternut may be shaped by both glaciation and modern environmental conditions. Pollen records and hindcast species distribution models combined with genetic distinctiveness in New Brunswick suggest that butternut may have persisted in cryptic northern refugia. We suggest that thorough sampling across a species range and evaluating multiple lines of evidence are essential to understanding past species movements. Data was cleaned and processed in R - genetic data cleaning and analyses and species distribution modeling methods were performed in Emily Schumacher's butternut repository and fossil pollen data cleaning and modeling was performed in Alissa Brown's juglans repository. Steps for performing data cleanining, analyses, and generating figures for the manuscript are described within each repo.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. 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. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication: Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. Communications Earth and Environment. Publication abstract: Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.

Drawing upon climate change damage functions previously proposed in the literature that we have calibrated to a common level of damages at 2.5 C, we examine the effect upon the social cost of carbon (SCC) of varying the specification of damages in a DICE-like integrated assessment model. In the absence of risk aversion, all of the SCC estimates but one agree within a factor of two. The effect of varying calibration damages is mildly sublinear. With a moderate level of risk aversion included, however, the differences among estimates grow greatly. By combining elements of different damage specifications and roughly taking into account uncertainty in calibration, we have constructed a composite damage function that attempts to approximate the range of uncertainty in climate change damages. In the absence of risk aversion, SCC values calculated with this function are in agreement with the standard quadratic DICE damage function; with a coefficient of relative risk aversion of 1.4, this damage function yields SCC values more than triple those of the standard function.

#### Note: #### An updated version of these data including data on biofuels and fuels from waste is available [here](https://pub.uni-bielefeld.de/record/2950291). The extended version also offers a package of R-scripts that have been used to reproduce the statistical analysis presented in [Hötte, Pichler, Lafond (2021): The rise of science in low-carbon energy technologies](https://doi.org/10.1016/j.rser.2020.110654). This data publication offers data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this data publication and the following paper. Kerstin Hötte, Anton Pichler, François Lafond: *The rise of science in low-carbon energy technologies*, Renewable and Sustainable Energy Reviews, Volume 139, 2021 [https://doi.org/10.1016/j.rser.2020.110654](https://doi.org/10.1016/j.rser.2020.110654) ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate change. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources") and Y02E03 ("Energy generation of nuclear origin") technologies are used. 8 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: [10.5281/zenodo.3685972](10.5281/zenodo.3685972) The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 8 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fiels were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assiged. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) #### Note: #### The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - Number of papers in MAG: 179,083,029 - Number of all patents: 10,160,667 - Number of citing patents: 2,058,233 - Number of cited papers: 4,404,088 - Number of citation links from patents to papers: 34,959,193 LCET subset: - Number of LCET patents: 57,530 - Number of citing LCET patents: 16,674 - Number of cited papers: 53,509 - Number of citation links from LCET patents to papers: 151,253 - Number of citation links from LCET patents to other patents: 567,274 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >250,000 hierarchical CPC classes, 8 LCET types Citation links: - Reference type, citation type, reliability score #### If you have further questions about the data or suggestions, please contact: kerstin.hotte@oxfordmartin.ox.ac.uk ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered gas resources of the Sacramento Basin province in California. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

Poleward range extensions by warm-adapted sea urchins are switching temperate marine ecosystems from kelp-dominated to barren-dominated systems that favour the establishment of range-extending tropical fishes. Yet, such tropicalization may be buffered by ocean acidification, which reduces urchin grazing performance and the urchin barrens that tropical range-extending fishes prefer. Using ecosystems experiencing natural warming and acidification, we show that ocean acidification could buffer warming-facilitated tropicalization by reducing urchin populations (by 87%) and inhibiting the formation of barrens. This buffering effect of CO2 enrichment was observed at natural CO2 vents that are associated with a shift from a barren-dominated to a turf-dominated state, which we found is less favourable to tropical fishes. Together, these observations suggest that ocean acidification may buffer the tropicalization effect of ocean warming against urchin barren formation via multiple processes (fewer urchins and barrens) and consequently slow the increasing rate of tropicalization of temperate fish communities. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2021) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2021-07-26.

This data release contains the boundaries of assessment units and input data for the assessment of Oil and Gas Resources in the Mancos-Menefee Composite and Underlying Todilto Total Petroleum Systems of New Mexico and Colorado. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.