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zenodo44/100

Data from an investigation into colibactin-producing Escherichia coli endemicity globally

This upload is a part of the study "Geographical variation in the incidence of colorectal cancer and urinary tract cancer is associated with population exposure to colibactin-producing Escherichia coli" published in Lancet Microbe on 5 December 2024, doi: <a href="https://doi.org/10.1016/j.lanmic.2024.101015">10.1016/j.lanmic.2024.101015</a>. Contents Data and scripts from the "Geographical variation in colorectal and urinary tract linked cancer incidence is associated with population exposure to colibactin-producing Escherichia coli" study. For the E. coli assembly collection presented in the study, see the separate upload at <a href="../records/13374348">https://zenodo.org/records/13374348</a>.

opencc-by-4.0Sep 2024View details →

zenodo44/100

MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"

Corresponding peer-reviewed publication This dataset corresponds to all input and output files that were used in the study reported in: <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. We aim to answer two questions: 1.     What is the intrinsic spatial resolution of global river dynamics? 2.     How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes? Data sources The following sources were used to produce files in this dataset: <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> Software The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling. Data Products The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii). Riv_coast.zip contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean. ·       riv_coast.zip     o      cor: riv_coast_pfaf_ii_COR.shp     o      uncor: riv_coast_pfaf_ii_UNCOR.shp   Qout_rivwidth.zip contains csv files of the aggregate river discharge to the ocean (km3/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       Qout_rivwidth.zip: Qout_pfaf_ii_rivwidth.csv   V_rivwidth_low.zip contains csv files of the aggregate river storage (km3) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_low.zip: V_pfaf_ii_rivwidth_low.csv   V_rivwidth_nrm.zip contains csv files of the aggregate river storage (km3) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_nrm.zip: V_pfaf_ii_rivwidth_nrm.csv   V_rivwidth_hig.zip contains csv files of the aggregate river storage (km3) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_hig.zip: V_pfaf_ii_rivwidth_hig.csv   Largest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins. ·       largest_rivs.zip     o      cat: cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins     o      csv: Q_df_top10.csv – total discharge contributed by each basin     o      riv: riv_top10_nxx.shp – river reaches that drain the 10 largest basins   Smallest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m. ·       smallest_rivs.zip     o      cat: cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii     o      csv: Q_df_top10.csv – total discharge to the ocean from each narrow river reach     o      riv: riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii   Global_summary.zip contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage. ·       global_summary.zip     o      Qout_rivwidth: global summary files for discharge to the ocean (km3/yr) under river width sampling     o      V_rivwidth_low: global summary files for total river storage (km3) for the low residence time scenario under river width sampling     o      V_rivwidth_nrm: global summary files for total river storage (km3) for the normal (medium) residence time scenario under river width sampling     o      V_rivwidth_hig: global summary files for total river storage (km3) for the hig residence time scenario under river width sampling     o      cat_small_gl: cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean   Rivwidth_sens.zip contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations. ·       Rivwidth_sens.zip     o      riv_coast     o      Qout_rivwidth_VIC     o      Qout_rivwidth_CLSM     o      Qout_rivwidth_NOAH     o      V_rivwidth_low_VIC     o      V_rivwidth_nrm_VIC     o      V_rivwidth_hig_VIC     o      V_rivwidth_low_CLSM     o      V_rivwidth_nrm_CLSM     o      V_rivwidth_hig_CLSM     o      V_rivwidth_low_NOAH     o      V_rivwidth_nrm_NOAH     o      V_rivwidth_hig_NOAH     o      global_summary_VIC     o      global_summary_CLSM     o      global_summary_NOAH   Cor_sens.zip contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis. ·       Cor_sens.zip     o      Qout_rivwidth_ENS     o      V_rivwidth_low_ENS     o      V_rivwidth_nrm_ENS     o      V_rivwidth_hig_ENS     o      global_summary_ENS   Width_val.zip contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018). ·       Width_val.zip: width_validation_pfaf_ii.csv   Known bugs in this dataset or the associated manuscript No bugs have been identified at this time.   References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. https://doi.org/10.1126/science.aat0636 Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-024-01421-5 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. https://doi.org/10.1029/2019WR025287 Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

opencc-by-sa-4.0Sep 2024View details →

zenodo44/100

Global Biotic Interactions: Interpreted Data Products hash://md5/e76bf914309ad27dce6ab911d8854590 hash://sha256/ba79836caab5b7ba2d7d659123d27c89f4ad990bd50f97ded935edee9fbe9f87

Global Biotic Interactions: Interpreted Data Products Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit.  Citation -------- GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects. To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI: Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005. Bias and Errors -------- As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product. To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found. This version was preceded by [7].  Contents -------- README: this file citations.csv.gz: contains data citations in a in a gzipped comma-separated values format. citations.tsv.gz: contains data citations in a gzipped tab-separated values format. datasets.csv.gz: contains list of indexed datasets in a gzipped comma-separated values format. datasets.tsv.gz: contains list of indexed datasets in a gzipped tab-separated values format. verbatim-interactions.csv.gz contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. verbatim-interactions.tsv.gz contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  interactions.csv.gz: contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. interactions.tsv.gz: contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-interactions.csv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-interactions.tsv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources. refuted-verbatim-interactions.csv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  refuted-verbatim-interactions.tsv.gz: contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.  interactions.nq.gz: contains species interactions expressed in the resource description framework in a gzipped rdf/quads format. dwca-by-study.zip: contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension. dwca.zip: contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension. neo4j-graphdb.zip: contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data. taxonCache.tsv.gz: contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format. taxonMap.tsv.gz: describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format. References ----- [1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005. [2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557. [3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472 [4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400. [5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978. [6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955. [7] GloBI Community. (2024). Global Biotic Interactions: Interpreted Data Products hash://md5/946f7666667d60657dc89d9af8ffb909 hash://sha256/4e83d2daee05a4fa91819d58259ee58ffc5a29ec37aa7e84fd5ffbb2f92aa5b8 (0.7) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11552565 Content References ----- hash://sha256/5f4906439eba61f936b3dd7455a62c51656a74206f82d3f654e330fda6fbbe45  citations.csv.gz hash://sha256/c8100368dae39363b241472695c1ae197aaddc6e3d6c0a14f3f5ee704b37f3f6  citations.tsv.gz hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f  datasets.csv.gz hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f  datasets.tsv.gz hash://sha256/f11dc825609cdb1d4a3e9ba8caca9bf93c90dd6f660c7f6a0c8aa01c035a5e1f  dwca-by-study.zip hash://sha256/7f16aacacae74e8b0cdef04c612ba776f508ff7ffe385abc57583e37aec8fe53  dwca.zip hash://sha256/b65e4c9a3615f1386bb97e45fb907d053df55476149aa6d71e6f398351218d0d  interactions.csv.gz hash://sha256/0c28032392f82d753690be126805e6334ca46bdc4b5e2102a79b15ce0cc0ba90  interactions.nq.gz hash://sha256/8a7031250c288ba0da3d5cdbedc19d54c2f16ba3aa70d49826a7369b6edeca04  interactions.tsv.gz hash://sha256/d0c0fbf536cc63c004d057efc14600ba8cc5874f401b08f51837273b7854f1bb  neo4j-graphdb.zip hash://sha256/50e77636f8b58c040e38b6a70ba7cc8288b190ef252dc0d4eb2f12f4c541e82f  README hash://sha256/a74e2a39cfe133ae9de1eeea94f5dda8cbd58cfe61a8ccf91b7c540757719c74  refuted-interactions.csv.gz hash://sha256/37b06e274e41ca749399763989816854101238ade9863365f384a2764c639e9d  refuted-interactions.tsv.gz hash://sha256/23315b6cd3fdc91f9c1d5d5bc39fa52cf1cef7a4e97d9d023d452751df13f30e  refuted-verbatim-interactions.csv.gz hash://sha256/ff82e40cee4f8a8852d0c241f5027f66157a2b8a9090ffa3a0a329a206828d96  refuted-verbatim-interactions.tsv.gz hash://sha256/f072fbc7affb6e29978c7540af6cdccd3a219a23b0a4765b5bae56bd20df0d88  taxonCache.tsv.gz hash://sha256/cd28c81bb2432646a81ad216bc11818f7568ce81826e0074d9a33579da2c1426  taxonMap.tsv.gz hash://sha256/a1d14aa47806c624cf7e3a8c8236643dcf19ed1835c79c65958f7317ebfb9566  verbatim-interactions.csv.gz hash://sha256/2284434219d5fdab1e2152955f04363852c132b76709c330d33e31517817a82e  verbatim-interactions.tsv.gz hash://md5/d6ebf42729d988e15cb30adfa6112234  citations.csv.gz hash://md5/42877ae68e51871b8eb7116e62f6b268  citations.tsv.gz hash://md5/3e437580296fdeff3b6f35d1331db9d1  datasets.csv.gz hash://md5/3e437580296fdeff3b6f35d1331db9d1  datasets.tsv.gz hash://md5/fe88720fd992771bd64bfa220ad6a7d3  dwca-by-study.zip hash://md5/cbe132a9288feaef2f3e0c0409b8dc2f  dwca.zip hash://md5/051f6db667c4b84616223c2776464dbf  interactions.csv.gz hash://md5/b66857f8750e56ba9abe484b1f72eac4  interactions.nq.gz hash://md5/300839c346184b2fedc4e1fb31bcc29c  interactions.tsv.gz hash://md5/e79cf5ffee919672f99ea338f3661566  neo4j-graphdb.zip hash://md5/898678f47561d7ef53722bc32957dcd9  README hash://md5/65a185f19df304e53f92a7275f2de291  refuted-interactions.csv.gz hash://md5/bc37a4354f8a2402e9335ae44f28cbd7  refuted-interactions.tsv.gz hash://md5/42e817c31e2ca05e582be94e6ec283c5  refuted-verbatim-interactions.csv.gz hash://md5/93639b70a1d8e47fd194b6384c0287a7  refuted-verbatim-interactions.tsv.gz hash://md5/e32482b3697aa928a5fcb58a570191df  taxonCache.tsv.gz hash://md5/75251510925875d3fdc1952cc4b98043  taxonMap.tsv.gz hash://md5/6a0c6224f4a4c3dca9994d70ad0b2fd2  verbatim-interactions.csv.gz hash://md5/905acb49a700e5b5a292be02c917e710  verbatim-interactions.tsv.gz

opencc-zeroJun 2024View details →

zenodo44/100

Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products

v2 update: <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> The v2 data is used for the final published version of the Global Carbon Budget 2024. ----------------- Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.   Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual. Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output." Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

opencc-by-4.0Nov 2024View details →

zenodo44/100

Data for "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime"

This contains measured data for paper "Lithium isotope evidence shows Devonian afforestation may have significantly altered the global silicate weathering regime", under funding of ERC grant 682760 CONTROLPASTCO2.  This consists all the Li isotope data obtained from brachiopods/bulk carbonate  samples.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction

This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution.  Also reported is the variance.  Estimates correspond to this paper: Becker, J. S., DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, H. Wang, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and J. J. West (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, Elementa Science of the Anthropocene, 11: 1, doi: 10.1525/elementa.2022.00025. The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models.  Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time.  The estimates here are the final results using a weighted RAMP bias correction.

opencc-by-4.0Jan 2024View details →

zenodo44/100

CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000

CitiesGOER is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the TreeGOER (Tree Globally Observed Environmental Ranges) database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparations of these layers are provided by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. CitiesGOER was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site. The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by <a href="https://public.opendatasoft.com/explore/?sort=modified">Opendatasoft</a> and made available from <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name</a>. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.6-47) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>. Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>). Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5. The locations of the 52,602 cities are mapped in one of the series available from the TreeGOER Global Zones atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>. Version 2024.10 includes a new data set that documents the location of the city locations in Holdridge Life Zones. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>. Version 2024.11 includes a new data set that documents the location of the city locations in Köppen-Geiger climate zones. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a>   Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>). Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.   As an alternative to CitiesGOER, the ClimateForecasts database (<a href="https://zenodo.org/records/10776414">https://zenodo.org/records/10776414</a>) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees database</a> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>).   When using CitiesGOER in your work, cite this depository and the following: <ul> <li>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(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> </ul> When using information from the Holdridge Life Zones, also cite: <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> When using information from Köppen-Geiger climate zones, also cite: <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> When using information on the Whittaker biome types, also cite: <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> When using information on the steady state water table depth, also cite: <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul>   The development of CitiesGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. Development of version 2024.10 was further supported by the Green Climate Fund through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso project, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

opencc-by-4.0Jul 2023View details →

zenodo44/100

Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)

This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019. Data are contained in two NetCDF files: <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> Data characteristics <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> Related manuscript Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in Global Biogeochemical Cycles.

opencc-by-4.0Aug 2024View details →

zenodo44/100

DATA (part 2): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations

Data used for the peer-reviewed article published in the Journal of Climate, titled: "Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations." The publication is available at: https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0172.1/JCLI-D-21-0172.1.xml. The software developed for the data herein is available at: https://github.com/mariajmolina/climatico.

opencc-by-4.0Nov 2021View details →

zenodo44/100

DATA (part 1): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations

opencc-by-4.0Nov 2021View details →

zenodo44/100

WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data

WINTERC-G: A global, temperature and compositional model of the lithosphere            and upper mantle. Version:  v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli            Contact:  Javier Fullea (jfullea@ucm.es)           Facultad de Fisica,           Universidad Complutense de Madrid (UCM),           Spain           ////////           Geophysics Section,           Dublin Institute for Advanced Studies           Dublin, Ireland   TYPE:  This contains files with:  i) the model directly on the triangular grid solved for in the surface wave inversion.  ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion   If you have any questions regarding the methodology or the construction of the model, please contact the authors. If you use the model, we would request that you cite the reference indicated below, and appreciate your feedback regarding the model and its application. Citation: Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191. ******************************* Summary: construction of the model. WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at global scale. WINTERC-G is based on upon the integrated geophysical-petrological approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all relevant mantle rock physical properties modelled (seismic velocities and density) are computed within a thermodynamically self-consistent framework allowing for a direct parameterization in terms of the temperature and composition of the lithosphere-upper mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion, along with surface heat flow and elevation (isostasy) for temperature and crustal structure using a point-wise, non-linear, gradient-search inversion over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in order to refine the initial crustal density and mantle composition distributions from the step 1 for a fixed temperature field. The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)  and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary, the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330, and 400 km depths. The parameter space in step 2 is defined by the average crustal density, and the mantle composition in the lithosphere and sublithosphere. We use the output crustal density from step 1 as the initial value in step 2 inversion. Mantle densities are derived based on the output temperature field from step 1 (kept fixed) and the bulk mantle composition inversion variables.     ******************************* This archive contains the following files:   README (this file)   WINTERC-G_Vp-Vs.lis (triangular grid)   WINTERC-G_rad_anis_Vs.lis (triangular grid)   WINTERC-G_Temperature.lis (triangular grid)   WINTERC-G_Density.lis (triangular grid)   WINTERC-G_LAB.lis (triangular grid)   WINTERC_T_rho_1D.z (1D average model of temperature and density)   rho_*_out.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)   ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)   Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field) Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points): * WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km  Format for each column:  #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)      5640         93.72     4.135       -5.0                3.91     2.11 * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km   Format for each column:   #Column number longitude latitude depth(km, <0 downwards) anisotropy (%) * WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km  Format for each column:   #Column number longitude latitude depth (km, <0 downwards) T (ºC)   dT (%)   dT(K)         6437         297.20    -2.524     -259.000             1431.9   -1.91     -27.9   The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2). * WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km  Format for each column:   #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)   The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3). * WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km      5.00000000       0.0000000000000000        6.0259973839110526    3.00000000       0.0000000000000000        38.960571309690394    1.00000000      0.33634006819423840        174.42296045978722   -1.00000000       3.8888495253719624        1692.8437489147236   -3.00000000       23.974111923225379        1863.8834351235944   -5.00000000       47.727920701943034        2568.2414495590924   -7.00000000       89.633398074381162        2819.8386016341910   -9.00000000       137.01489361657013        2839.5325893195904   -11.0000000       182.35233447017222        2897.6600872935287   -13.0000000       224.46247069572485        2945.2036923862997   -15.0000000       260.63395547331390        3069.6809340323475   -17.0000000       292.28175449521456        3132.4574175461721   -19.0000000       322.29571965406632        3145.5747337463940   -21.0000000       351.58698283375054        3157.2401512748038   -23.0000000       380.30002225705056        3177.0000420059773   -25.0000000       408.50259805632055        3183.6651032398490   -27.0000000       436.22632217636487        3190.9586785996116   -29.0000000       463.48733903170023        3198.9369509456310   -31.0000000       490.29841705549831        3209.7229872383764   -33.0000000       516.71149258457456        3221.6329506091679   ... Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:   * rho_c_out.xyz: average crustal density   * rho_submoho_out.xyz: mantle density below the Moho discontinuity   * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.   Format for the density files:   # longitude latitude density (kg/m3)     Files containing layer discontinuities:  * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)  * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)  * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)  * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)   Format for the discontinuity files:    # longitude latitude depth (km)     The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius: 1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically) 2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz  with rho=910 kg/m3 (constant vertically) 3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically) 4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom) 5/ 20km-36km: from  z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km)   with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom) 6/ 36km-56km: from  z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km)   with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom) 7/ 56km-80km: from  z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom) The next 6 layers are computed using the constant radius option: 8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom) 9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom) 10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom) 11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom) 12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom) 13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)

opencc-by-4.0Jul 2021View details →

zenodo44/100

Global forest management data at a 100m resolution for the year 2015

We provide four data records: 1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes:  <ul> <li> “ID” is a unique location identifier  </li> <li> “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel.  </li> </ul> <ul> <li> “Land_use_ID “is a land use class:  <ul> <li>11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests.  </li> <li>20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc.  </li> <li>31 - Planted forest.  </li> <li>32 - Short rotation plantations for timber.  </li> <li>40 - Oil palm plantations.  </li> <li>53 - Agroforestry. </li> </ul> </li> <li> “Flag” identifies a data origin:  1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach. </li> </ul> 2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ". 3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif" 4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes:  <ul> <li> “ID” is a unique location identifier  </li> <li> “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel  in lat/lon projection  </li> <li> “first_landuse_class “is a land use class, as in (1).  </li> </ul> <ul> <li> “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence.  </li> </ul> 5. Original crowdsourced data set as a .csv table. 6. Compiled FAO FRA forest statistics and mapped classes by countries into one table (.csv format).

opencc-by-4.0Feb 2021View details →

zenodo44/100

Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.

PyPSA-Earth is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base. Since the GitHub codebase is not suited for handling large changing files, we provide here separate data bundles and cutouts to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a> The below-provided cutouts are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a> ECMWF ERA5 <ul> <li>Source: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li>Terms of Use: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>

opencc-by-4.0Jan 2022View details →

zenodo44/100

Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples

# Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples" SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes. SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results. SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID. SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows: * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020. * rab.all:  median clr value for all samples in the feature * rab.win.conditionA:  median clr value for the condition A of samples * rab.win.conditionB: median clr value for the condition B of samples * diff.btw: median difference in clr values between A and B conditions * diff.win: median of the largest difference in clr values within A and B conditions * effect : median effect size: diff.btw / max(diff.win) for all instances * overlap : proportion of effect size that overlaps 0 (i.e. no effect) * we.ep: Expected P value of Welch’s t test * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test * wi.ep: Expected P value of Wilcoxon rank test * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test * parts: gene name * conditionA: label of condition A that is compared against condition B * conditionB: label of condition B that is compared against condition A * conditions.A.vs.B: label to explain condition A compared against condition B NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences. SD6: FASTA file containing all unique consensus sequences reported in the manuscript. SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective

Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. Functional Ecology, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'

This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'. The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the raster package in R. Note that the unit is ppmv. Please note that both of these files must be in the same directory to open in R properly. Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn

opencc-by-4.0Mar 2022View details →

zenodo44/100

"Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data

This dataset and code is part of the following publication: Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7   There are three zip-folders accompanying this publication: Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors. Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository. TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"

This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.

opencc-by-4.0May 2022View details →

zenodo44/100

Globally-gridded data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting globally-gridded data products for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415) along with ancillary data on climate, vegetation, and soil characteristics to produce spatially-explicit global estimates of mineral-associated soil organic carbon stocks (MOC) and mineralogical carbon capacity (MOCmax) in non-permafrost, non-desert mineral soils. Globally-gridded datasets are given in kgC/m2 for topsoil (0-30cm) and subsoil (30-100cm) at 0.5 degree by 0.5 degree spatial resolution.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon

Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindu&scaron;ková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. Nature Communications, 2022. We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.

opencc-by-4.0Dec 2021View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record