Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
903
datasets available to search
ShareScore release 0.9.0
Dataset results
903 results for “domestic”
FIG. 8. — Size comparison within 3500-2700 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 8. — Size comparison within 3500-2700 BC. Statistics for the Czech early-mid Eneolithic based on the same data as in Fig. 3 (statistics are in Table 3). Statistics for Balatonőszöd (separately Boleráz and Baden phase) based on Vörös (2014); for Bronocice (incl. BR III-BR V, 3400-2700 BC, excl. 2 tibiae) based on Milisauskas et al. (2006); for German sites from Benecke & Driesch (2003) and Benecke (2006). Others as in Figs 3, 6. Abbreviations: CR, Czech Republic; KH-Denemark/KH-D, Kutná Hora-Denemark site; TRB, Funnel Beaker c.
FIG. 6 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 6. — Size comparison between horses from the Czech Republic (dark boxes and dots) and adjacent regions (light boxes) within: A, 15000-5600 BC; B, 5600- 4200 BC. X-axis, locations (in brackets: culture and/or country). Statistics for Hadí cave and Těšetice-Kyjovice based on the same data as the Magdalenien and early Lengyel, respectively (in Fig. 3; Table 3). Statistics for Mirnoe, Kniegrotte, Bärenkeller, LBK (Germany), Sakarovka and Dereivka taken from Benecke & Driesch (2003), for Lausnitz based on Teichert (1963), for Szabadszállás-Tözegtelep on Vörös (1981). Others as in Fig. 3. Abbreviations: CR, Czech Republic; LBK, Linear Pottery c.
FIG. 4 in Horse size and domestication: Early equid bones from the Czech Republic in the European context
FIG. 4. — LSI distributions of Equus postcranial breadths/depths based on Czech datasets from selected periods and E. przewalskii presented by histograms. Based on the same data as in Fig. 3 (outliers incl.). Period codes correspond to other Tables and Figures. A, period 1 – Late Paleolithic (Magdalenien); B, pe- riod 3 – Early Lengyel (Těšetice-Kyjovice site); C, period 6 – Early Eneolithic (TRB); D, period 8 – Middle Eneolithic (Řivnáč culture); E, period 10 – Early Bronze (Únětice culture); F, period 11 – Late Bronze (KnovÍz culture); G, Equus przewalskii; X axes, LSI scale; Y axes, frequency; Red curve, Kernel density; Dotted curve, fit (estimated) normal distribution. Analysed by Past 3.02 statistical software.
Replication R code and data for "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes"
<p>This repository holds various files and R code used in the publication of the manuscript entitled "Geometric morphometric investigation of craniofacial morphological change in domesticated silver foxes".</p> <p>Data files include: The 3D landmark coordinates of each individual specimen (Fox_data_Morphologika.txt), the linear measurement data associated with those foxes (fox_linear_volume_data.csv), and replication data measurements. </p> <p>The following files include the R code used to perform the analyses contained within the paper:</p> <p>1_Procrustes_analysis - details the Geometric morphometrics analyses performed</p> <p>2_linear_models - details the model specification for the GLS models employed in the paper</p> <p>3_graph_code - contains R script for the creation of the graphs displayed in the paper</p> <p>4_repeatability_script - contains R code that details the statistical calculations made with the repeatability measurements indicated above. </p> <p> </p> <p> </p> <p> </p>
tsammalex-data: Tsammalex 0.2 "domesticated animals"
<p>Christfried Naumann & Steven Moran & Guillaume Segerer & Robert Forkel (eds.) 2015. Tsammalex: A lexical database on plants and animals. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://tsammalex.clld.org)</p>
Data for: Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita at purchasing power parity (PPP) over 1990-2022
<p>This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022. </p> <p>The dataset is presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. <a href="https://doi.org/10.1038/s41597-025-04487-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04487-x</a></p> <p><strong>Code is available</strong> at: <a href="https://github.com/mattikummu/griddedGDPpc" target="_blank" rel="noopener">https://github.com/mattikummu/griddedGDPpc </a></p> <p> </p> <p><strong>The following data is given (formats in brackets)</strong></p> <ul> <li>GDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)</li> <li>Total GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) </li> <li>Input data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc </li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level<br><em>Product type:</em> GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_</p> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids for GDP per capita data:</em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided)</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 </p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Grids for total GDP:</em></p> <p>Resolution: 30 arc-sec, 5 arc-min or 30 arc-min</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec)</p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2022</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: USD in 2017 international dollars</p>
Data from: Fluctuation of ecological niches and geographic range shifts along chile pepper's domestication gradient
<p>Domestication is an ongoing well-described process. However, while many have stud- ied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (<em>Capsicum annuum</em>) in its center of origin (domestication). <em>C. annuum</em> is a major crop species globally whose center of domes- tication, Mexico, has been well-studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a con- stant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domes- tication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
Results for the paper "The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition"
<p>As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for<br>export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic<br>energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change<br>mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen<br>exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated.<br>Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can<br>steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance<br>among actors.</p>
The processed clean data of 16S rRNA V4 amplicon sequecnces for the six stage of phenolic microbiome domestication
Open the record for dataset details and reuse information.
Library of price and performance data of domestic and commercial technologies for low-carbon energy systems
<p>This library consists of extensive price and performance data of commercially available technologies for low-carbon energy systems on the UK market, including domestic and commercial applications. All information is obtained from published manufacturer datasheets and pricelists. The library contains useful information for energy-system and technology modellers in their efforts to capture the techno-economic characteristics of different technology options, minimise uncertainties and suggest reliable system- and technology-design strategies.</p> <p>The data analysis shows how technology characteristics vary with component choice, technology size and to capture the spread of values found between different suppliers, which can be used to determine uncertainty bounds on key performance indicators. Fitting techniques can be used to determine relationships arising from the collected data, infer values for which data are not available and quantify the related variability in technology characteristics.</p> <p>This work was conducted by members of the <a href="https://www.imperial.ac.uk/clean-energy-processes/">Clean Energy Processes (CEP) Laboratory</a> and is part of Project 2 of the <a href="https://www.imperial.ac.uk/energy-futures-lab/idles/">Integrated Development of Low-Carbon Energy Systems (IDLES)</a> project. IDLES brings together researchers across Imperial College London and partner organisations and companies to provide the evidence needed to facilitate a cost-effective and secure transition to a low-carbon future. The overarching aim of Project 2 is: (i) to characterise current and new/future technologies in terms of cost and performance to provide evidence for whole-energy system modelling; and (ii) to extend the capabilities of whole-energy-system models so that they can, apart from optimising energy network infrastructures, provide information to manufacturers about the optimal choice of materials, components and the design of key technologies.</p> <p>The library will be updated regularly as more data regarding existing and new technologies are collected.</p>
FIG. 6 in Biological and cultural history of domesticated dogs in the Americas
FIG. 6. — Selection of skulls of dog breeds originating in the Americas (selection not exhaustive). These dogs demonstrate the great variation in skull shape and body size of modern American breeds, from slender to short snouted and from giant to dwarf sized varieties, including hairless forms with oligodontia. A, Chesapeake Bay Retriever (NMBE 1051681);B, Alaskan Malamute (NMBE 1051387);C, Chihuahua (NMBE 1052001);D, Fila Brasileiro (I.f.H. 14005, mirrored);E, Mexican hairless dog (ZMUZH 13754); F, Peruvian hairless dog (NMBE 1062857); G, Boston Terrier (NMBE 1051959); H, Newfoundland (NMBE 1050502). Abbreviations: I.f.H., Zoologisches Institut/Populationsgenetik (former Institut für Haustierkunde), Christian-Albrechts-Universität zu Kiel, Germany; NMBE, collection of the Albert-Heim-Foundation at the Naturhistorisches Museum Bern, Switzerland;ZMUZH, Zoologisches Museum der Universität Zürich, Switzerland. Scale bar: 5 cm.
FIG. 3 in Biological and cultural history of domesticated dogs in the Americas
FIG. 3. — Drawings of skulls of pre-Columbian domestic dogs. These drawings were published as part of Alfred Nehring's work (1884) and show skulls of domestic dogs that have been excavated in the Inca burial ground of Ancon, Peru. The same skulls are also depicted in Figure 2. A, shepherd-like dog in ventral view; B, bulldog-like dog in ventral view; C, same dog as in B but from dorsal view. Drawings are not to scale.
FIG. 1 in Biological and cultural history of domesticated dogs in the Americas
FIG. 1. — "Canis mexicana", a domestic dog with peculiar humps and apparent muscle hypertrophy, as depicted in 1651 by Hernández in his Rerum medicarum Novae Hispaniae thesaurus (Hernández 1992). Previously dismissed as a caricature (Ueck 1961), it may actually illustrate a phenotype associated with mutations in the myostatin gene. Picture retrieved from Internet Archive https://archive.org/details/rerummedicarumno00hern/page/466/mode/1up, last consultation on 30 November 2021.
FIG. 5 in Biological and cultural history of domesticated dogs in the Americas
FIG. 5. — Photos of selected dog breeds originating in the Americas. A-J, dogs from North America; K-P, dogs from South and Central America and Cuba. All photo images are from Shutterstock, photo credit is indicated in parenthesis. A, Alaskan Malamute, 19 months old (Eric Isselee); B, Longhaired whippet (MirasWonderland); C, American bulldog puppy, five months old (Erik Lam); D, American cocker spaniel standing with reflection on white background, three years old (WilleeCole Photography); E, three American hairless terriers (Dora Zett); F, Chesapeake Bay retriever (Erik Lam); G, American Akita (Jagodka); H, Silken Windhound (Erik Lam); I, Siberian Husky, four years old (Eric Isselee); J, Miniature American shepherd (MirasWonderland); K, young chihuahuas (cynoclub); L, Xoloitzcuintli,Mexican hairless dog (Masarik); M, Peruvian hairless dog, one year old (Rosa Jay); N, young Bichon Havanese dog (Dorottya Mathe); O, Brazilian Fox-terrier (venturinirica); P, Argentinian Dogo (GeptaYs).
FIG. 2 in Biological and cultural history of domesticated dogs in the Americas
FIG. 2. — Remains of pre-Columbian domestic dogs as reported in the nineteenth century. Drawings of remains of domestic dogs excavated from the graves of Ancon in Peru, as depicted in Reiss & Stübel (1880-1886: plates 117, 118; picture modified to exclude the depiction of a fox-like animal). A, mummies, as well as skull with mandible of one mummy, of shepherd-like domestic dogs; B, skulls and mandibles of shepherd-like, dachshund-like, and bulldog-like domestic dogs as well as long bones of the forelimb of a dachshund-like domestic dog. Two of these skulls are also depicted in Figure 3. Available from the IberoAmerikanisches Institut (Preussischer Kulturbesitz), https://digital.iai.spk-berlin.de/viewer/image/1681616637/225/ and https://digital.iai.spk-berlin.de/viewer/ image/1681616637/229/, last consultation on 30 November 2021. Drawings are not to scale.
FIG. 4 in Biological and cultural history of domesticated dogs in the Americas
FIG. 4. — Skulls of putative pre-Columbian domestic dogs at the Museum für Naturkunde Berlin, Germany. Skulls are housed in the Nehring-Collection (Zoologische Sammlung der KÖniglichen Landwirtschaftlichen Hochschule zu Berlin). A, "Inca dog" from Ancon (Zm 355), Peru (Nehring 1884), annotated as "Inca dog from Ancon, 1889" (as noted in the collection catalogue [1886] and as written on the actual specimen). This specimen most likely does not belong to the collection described by Nehring (1884: fig. 3) and Reiss & Stübel (1880-1886: fig. 1), which also originated from the graves in Ancon, Peru, but which are assumed to have been destroyed in the World Wars (Ueck 1961; B, C, Pre-Columbian dogs (7031 and 7013) from Puebla, Mexico, Nehring-Collection. Specimens are without present-day collection numbers. Every skull is depicted in dorsal, ventral, and lateral view (from left to right), where the lateral view of B is mirrored. B, "7031" and C, "7013" although also housed in the Nehring-Collection, were found by Prof. Seler in Berlin (as described in the collection files). In contrast to A, these apparently unpublished remains are described as "from an old grave in the district Chalchicomula, close to Jalapazco, Puebla Mexico". Further, both skulls are described as to be "surely from a time before the conquest of Mexico". Scale bar: 5 cm.
FIG. 7 in Biological and cultural history of domesticated dogs in the Americas
FIG. 7. — Cranium of a Salish woolly dog (Specimen #1) excavated in 1977 from the Semiahmoo Spit, WA (45WH17), dated to 900-420 years BP (Montgomery 1979): A, lateral view; B, dorsal view; C, occlusal view. Scale bar: 1 cm.
The case of 100% electrification of domestic heat in Great Britain
<p>Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.