Find research datasets worth reusing

opencc-by-4.0Apr 2019View details →

Ultrasound Vevo 2100 data on ascending and abdominal aneurysms in ApoE-deficient mice - baseline and early stage

This dataset contains raw data of ultrasound measurements taken of the ascending and abdominal aorta of Ang II-infused mice. Data were taken at baseline (prior to pump implantation) and at an early stage of disease development. Data can be openend with the Vevo 2100 analysis software provided by Fujifilm.

opencc-by-sa-4.0Jul 2019View details →

Baseline Detection in Arabic-script Manuscripts Dataset (BADAM)

A freely licensed dataset of 400 annotated Arabic-script manuscript pages for baseline detection. Refer to the <a href="https://hal.archives-ouvertes.fr/hal-02167164">article</a> for more information.

opencc-by-4.0Oct 2019View details →

Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office. The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency. The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below: <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>

opencc-by-4.0Jun 2023View details →

РИС. 1. Карта покаЗываюЩаЯ местоположение банки СаЯ де МальЯ на Маскаренском плато. На цветовой планке дана градациЯ Значений иЗобат (по Baseline Study. Indian Ocean Expedition. Monaco Explorations – www.monacoexplorations. org). in A new species of Chicomurex (Gastropoda, Muricidae) from the Saya de Malha Bank, Western Indian Ocean

РИС. 1. Карта покаЗываюЩаЯ местоположение банки СаЯ де МальЯ на Маскаренском плато. На цветовой планке дана градациЯ Значений иЗобат (по Baseline Study. Indian Ocean Expedition. Monaco Explorations – www.monacoexplorations. org).

opencc-by-4.0Aug 2020View details →

Fig. 6 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem

Fig. 6. Photos of specimen of Lygosoma samajaya Karin, Freitas, Shonleben, Grismer, Bauer, and Das, 2018 encountered in Zamrud National Park, Riau, Indonesia.

opencc-by-4.0Aug 2020View details →

Fig. 4 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem

Fig. 4. Species accumulation curve illustrates the accumulation of the encountered species during the 15 days of field sampling.

opencc-by-4.0Aug 2020View details →

Fig. 3 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem

Fig. 3. Eight of the 33 species found in this survey. (A) Limnonectes malesianus; (B) Leptobrachium nigrops; (C) Chalcorana parvaccola; (D) Pulchrana rawa; (E) Gonocephalus liogaster; (F) Cyrtodactylus majulah; (G) Cuora amboinensis; (H) Tropidolaemus wagleri.

opencc-by-4.0Aug 2023View details →

Fig. 1 in Baseline study of the morphological and genetic characteristics of Haemoproteus parasites in wild pigeons (Columba livia) from paddy fields in Thailand

Fig. 1. Haemoproteus columbae from the blood of wild pigeons (Columba livia); cytochrome b lineage HAECOL1 (a–d), COLIV03 (e–f) and COQUI05 (i–l); macrogametocytes (a–b; e–f; i–j) and microgametocytes (c–d; g–h; k–l); simple arrows: nuclei of erythrocytes; short triangle-head arrows: volutin granules; long trianglehead arrows: pigment granules; scale bar = 10 μm.

opencc-by-4.0Aug 2023View details →

Fig. 2 in Baseline study of the morphological and genetic characteristics of Haemoproteus parasites in wild pigeons (Columba livia) from paddy fields in Thailand

Fig. 2. Haplotype network of partial cyt b sequence (479 bp) of Haemoproteus columbae from wild pigeons (Columba livia); (A) the three common haplotypes (HAECOL1, COLIV03 and COQUI05) found in this study and proportions between study sites; (B) proportions of each haplotype reported worldwide and in the two study sites; Nakhon Sawan (purple), Phitsanulok (pink), and previous reports (gray); number of sequences shown in a pie chart of the network, without number inside indicates one sequence. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

FIGURE 4 in Building a baseline: a survey of the composition and distribution of the ichthyofauna of Guanabara Bay, a deeply impacted estuary

FIGURE 4 | Distribution of absolute richness (A) and species density (B) per km2 at Guanabara Bay, Rio de Janeiro, Brazil.

FIGURE 3 in Building a baseline: a survey of the composition and distribution of the ichthyofauna of Guanabara Bay, a deeply impacted estuary

FIGURE 3 | Ichthyofauna rates found for Guanabara Bay, Rio de Janeiro, Brazil. A. As a whole and for the B. Lower, C. Middle, and D. Upper estuaries separately, where "mg" corresponds to the number of extra samples needed to reach a "g" for estimated richness. When the line touches the x axis (mg = 0), the "g" values are reached by our study, that is, the value in which extra collections are not necessary.

FIGURE 2 in Building a baseline: a survey of the composition and distribution of the ichthyofauna of Guanabara Bay, a deeply impacted estuary

FIGURE 2 | Rarefaction curves for Guanabara Bay's ichthyofauna richness, Rio de Janeiro, Brazil. A. For the bay as a whole, and for the estuary compartments separately: B. Lower estuary, C. Middle estuary and D. Upper estuary.

FIGURE 1 in Building a baseline: a survey of the composition and distribution of the ichthyofauna of Guanabara Bay, a deeply impacted estuary

FIGURE 1 | Guanabara Bay map, Rio de Janeiro, divided into five km x five km quadrants. Different shades of blue indicate which estuary compartment (upper, middle or lower) the quadrant belongs to.

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

Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea

Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database. <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis: <ul> <li>One set of bioclimatic variables included BIO01 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (mean annual temperature), which is the single bioclimatic variables available for the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> Calculations were made with similar scripting pipelines in the R statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification. The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 18 species not documented by the TreeGOER.   References <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <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>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> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul>   Funding The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Fishes in MZNA-VERT: baseline freshwater sampling campaigns.

Natural history specimen data linked to collectors and determiners held within, "Fishes in MZNA-VERT: baseline freshwater sampling campaigns". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9">https://bionomia.net/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9">https://gbif.org/dataset/5f15bccf-9f93-41c9-8de3-34887b2a76c9</a>. Formatted as a Frictionless Data package.

Fig. 9 in The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

Fig. 9. Shelly residue from the Caspian Sea floor on the North-Middle Caspian Basin transition offshore Kazakhstan (44°43.4 N, 50°13.2 E; water depth 8 m). The soft bottom fauna is dominated by Holocene invasives such as (1) Abra segmentum, (2) Cerastoderma spec. Asensu Wesselingh et al. (2019), and (3) Mytilaster minimus. Pontocaspian endemics occur in this sample: (4) Monodacna albida s.l., (5) Didacna spp., (6) Dreissena caspia and (7) Turricaspia meneghiniana, yet these all are discolored and presumably pre-20th century).

Fig. 4 in The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

Fig. 4. Cardiidae. (1) Adacna laeviuscula; (a) RGM.1309812 LV; (b) RGM.1309813 RV. (2) Adacna minima; (A) RGM.1309811 LV; (b) RGM.1309810 RV. (3) Monodacna semipellucida, RGM.1309802 RV; (4) Monodacna caspia s.l. (a) RGM.1309803 LV; (b) RGM.1309804 RV. (5) Hypanis plicata; (a) RGM.1309808 LV (b) RGM.1309809 RV. Scale bars = 1 cm.

Fig. 3 in The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

Fig. 3. Rarefaction curve of Selitrennoye diversity with 95% confidence interval and extrapolated richness. The triangle indicates the observed richness.

Fig. 1 in The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

Fig. 1. Caspian Sea today (left panel) and during the Late Pleistocene Hyrcanian regional stage (right panel) (modified after Neubauer et al., 2018). The study site of Selitrennoye is indicated with a red star. Hyrcanian lake level was modeled in ESRI ArcGIS 10.4 based on Krijgsman et al. (2019), who suggested an absolute lake level of 30 m above sea level. (i.e., 57 m higher than today) at that time following Popov (1983). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6 in The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

Fig. 6. Shape variation in Didacna subcatillus RGM.1310272. (1) Variability of dentition in LV: (a) thick hinge, to (d) thin hinge. (2) Variability of dentition in RV: (a) thick hinge, to (d) thin hinge. (3) Shape variability of LV (a) oval (b) oval/triangular, (c) triangular. Scale bars = 1 mm.