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11,718 results for “life”
Life cycle inventories for on-road vehicles
<p>Life cycle inventory datasets for current on-road vehicles in Switzerland and Europe. These datasets can be consumed by brightway2 (https://brightway.dev/) and Simapro 9.x (https://simapro.com/), and link to either ecoinvent 3.6 (cut-off), ecoinvent 3.7.1 (cut-off), ecoinvent 3.8 (cut-off), ecoinvent 3.9 (cut-off) or UVEK:2018.</p> <p>These datasets can be cited as:</p> <p>Sacchi, R., Bauer, C. (2023) Life cycle inventories for on-road vehicles. Paul Scherrer Institut, Villigen, Switzerland.</p>
Assessment of current and future invasive plants in protected dune habitats of the Atlantic coastal region for the LIFE DUNIAS project (LIFE20 NAT/BE/001442)
<p>This .csv file contains the raw data from the risk screening supplementing the LIFE DUNIAS horizon scan for (invasive) alien species in protected habitats of Atlantic coastal dune ecosystems (<a href="https://doi.org/10.21436/inbor.86703335">Adriaens et al. 2022</a>). We gladly refer to the annexes and methods section in this report for more explanation about the fields and their contained values.</p> <p>The file contains the following fields:</p> <p><em>TaxonName</em>: original taxonomic name of the considered alien species</p> <p><em>WorkName</em>: taxonomic name of the considered alien species after lumping of subspecies, closely related species of a complex, functionally similar species of the same genus (see chapter 3.1)</p> <p><em>hab_xxxx</em> (1110, 1130, 1140, 1210, 1230, 1310, 1320, 1330, 2110, 2120, 2130, 2140, 21A0, 2150, 2190, 2160, 2170, 2180): susceptibility of habitat for the alien species (4-digit code refering to the Annex I habitat under the Habitats Directive) </p> <p><em>occ_XX</em> (BE, FR, IE, NL, ES, UK, DK, DE, PT, ALL): occupancy of the alien species in different countries of the Atlantic European region (as the number of 10km<sup>2</sup> squares per country). Country codes: BE = Belgium, FR = France, IE = Ireland, NL = Netherlands, ES = Spain, UK = United Kingdom, DK = Denmark, DE = Germany, PT = Portugal, ALL = total for all countries.</p> <p><em>scor_XXX_xxxx</em>: score of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies) conf_<em>XXX_xxxx</em>: confidence on the scores of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies)</p> <p><em>scor_ALL_MAX</em>: maximum ecological impact score of the alien taxon across all habitats</p>
Covid-19 - Symptoms - Impact on quality of life and needs of affected people
<p>Dataset « Covid-19 - Symptoms - Impact on quality of life and needs of affected people ». The data came from an online study involving a sample of 639 participants resident in France affected by COVID-19 symptoms several days, weeks or months after infection. It was collected to provide characterization of a wide range of symptoms of COVID-19, their effects on quality of life and the needs of those affected.</p>
Effects of life stage on the sensitivity of Folsomia candida to four pesticides
<p>This submission provides R-code and data files for our peer-reviewed work.</p><p>The R-notebook "Analysis_likelihood_ratio_test" contains the code used to estimate the parameters of concentration-response curves (EC10, EC50, LC10, LC50, and slopes) and perform likelihood ratio tests to compare curves from different tested life stages.</p><p>The R-notebook "Figures_Concentration_response_curves" showcases the code used to generate the figures presented in the manuscript.</p><p>The dataset files are provided in CSV format with Comma Separated Values:</p><ul><li>Cyproconazole_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Imidacloprid_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Teflubenzuron_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Thiacloprid_FolsomiaCandida_10days_20days_RawData_New.csv</li></ul><p>The submission includes the following:</p><ul><li>R files: R notebooks described above.</li><li>CSV files: Count data of springtail juveniles and adults.</li></ul><p> </p><p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 859891.</p><p>This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains.</p>
Dataset of "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters"
<pre>Two files are available as a result of data extraction from the 51 studies included in the systematic review entitled: "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters": (1) DataExtraction.csv -> Dataset resulting from data extraction. (2) DictionaryVariables.csv -> dictionary of the variables included in the dataset: Authors, doi, Year of publication, Study type, Design type, Data analysis strategy, Type of task, Objects, Infants' age (months), Context of interaction, and Country. </pre>
Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies
<p><strong><em>Supplementary Material - Filled LCI data collection schemes </em></strong>from the publication <em><strong>"Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies"</strong></em>. This repository includes LCI data for three time horizons; short-term, medium-term, and long-term.</p> <p>In the <strong>short-term time horizon</strong>, LCI data for the following technologies distinguished according to two different configurations (conventional and GT-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_short-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_short-term_GT-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_short-term_v01.xlsx)</li> <li>Battery EOL (GENESIS_LCI_battery_EoL_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery Li-ion (GENESIS_LCI_battery_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_short-term_v01.xlsx)</li> <li>Power electronics and drives (GENESIS_LCI_power_elec_drives_short-term_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_short-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_short-term_GT-bat_v01.xlsx)</li> <li>SAF (GENESIS_LCI_SAF_short-term_v01.xlsx)</li> </ul> <p>In the <strong>medium-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, GT-bat, and PEMFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_medium-term_v01.xlsx)</li> <li>Battery EOL Li-S GT-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery EOL Li-S PEMFC-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-S GT-bat (GENESIS_LCI_battery_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery Li-S PEMFC-bat (GENESIS_LCI_battery_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_medium-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_medium-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage (GENESIS_LCI_H2_onboard_storage_medium-term_PEMFC_v01.xlsx)</li> <li>Power electronics and drives GT-bat (GENESIS_LCI_power_elec_drives_medium-term_GT-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_medium-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_medium-term_GT-bat_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_medium-term_PEMFC-bat_v01.xlsx)</li> </ul> <p>In the <strong>long-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, PEMFC-bat, and SOFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_long-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_long-term_PEMFC-bat_v01.xlsx)</li> <li>Airframe SOFC-bat (GENESIS_LCI_airframe_long-term_SOFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_long-term_v01.xlsx)</li> <li>Battery EOL Li-Air PEMFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery EOL Li-Air SOFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery Li-Air PEMFC-bat (GENESIS_LCI_battery_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-Air SOFC-bat (GENESIS_LCI_battery_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_long-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_long-term_PEMFC-bat_v01.xlsx)</li> <li>Fuel cell SO (GENESIS_LCI_fuel cell_SO_long-term_SOFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage PEMFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage SOFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_SOFC-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_long-term_PEMFC-bat_v01.xlsx)</li> <li>Power electronics and drives SOFC-bat (GENESIS_LCI_power_elec_drives_long-term_SOFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_long-term_conventional_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_long-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant SOFC-bat (GENESIS_LCI_powerplant_long-term_SOFC-bat_v01.xlsx)</li> </ul> <p>Additionally, the following file is used for <strong>all time horizons</strong>:</p> <ul> <li>H<sub>2</sub> production and supply (GENESIS_LCI_H2_production_&_supply_v01.xlsx)</li> </ul>
A core ontology for modeling life cycle sustainability assessment on the Semantic Web with Accompanying Database
<p>To enable and support the uptake of semantic ontologies, we present a core ontology developed specifically to capture the data relevant for life cycle sustainability assessment. We further demonstrate the utility of the ontology by using it to integrate data relevant to sustainability assessments, such as EXIOBASE and the Yale Stocks and Flow Database to the Semantic Web. These datasets can be accessed by the machine-readable endpoint using SPARQL, a semantic query language.</p>
Dataset for publication "Influence of precursor morphology and cathode processing on performance and cycle life of sodium-zinc chloride (Na-ZnCl2) battery cells"
<p>High-temperature sodium-metal battery; sodium-metal halide battery (ZEBRA); molten-salt battery; zinc battery for stationary energy storage; alkali metal anode.</p> <p>Datasets used in the above manuscript. </p>
BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>More information</em></p> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.</p>
The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution
<p>Supplementary material for the article "The World Asellidae database and phylogeny: a collaborative backbone resource for comparative studies of subterranean life evolution"</p> <p>- SI Figure 5: The World Asellidae phylogeny with credibility Intervals for the age of the nodes. Node labels of the phylogeny indicate the 95% credibility intervals of the estimated dates.</p> <p>- SI Table 1: Metadata for the 2093 COI sequences used in the study.</p> <p>- SI Table 4: Alignment of the 2093 COI sequences used for the delimitation of MOTUs.</p> <p>- SI Table 5: Alignment of the 424 COI sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 6: Alignment of the 424 16S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 7: Alignment of the 424 FASTKD4 sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 8: Alignment of the 424 28S sequences used for the four-gene dated phylogeny.</p> <p>- SI Table 9: Metadata for the DNA sequences used for the 4-gene dated phylogeny.</p> <p>- SI Table 11: Data on body size, sexual body size dimorphism, habitat specialization and habitat size used in comparative analyses.</p> <p>- SI Table 12: Metadata for the DNA sequences deposited in NCBI as part of this study.</p>
Data describing the life cycle and material flows of neodymium contained in products
<p>Assumptions used to calculate flows of neodymium (Nd) in Europe. For various products (consumer products and industrial goods), the dataset describes the following properties:</p> <ul> <li>lifespan,</li> <li>product weight,</li> <li>neodymium content,</li> <li>end-of-life (EoL) fate,</li> <li>component weight,</li> <li>market share of Nd-containing components</li> </ul> <p>The classification of products is based on UNU Keys.</p>
Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies
<p>Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies</p> <p><strong>Abstract</strong></p> <p>Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Morphology contains the following files:</p> <p>wing_morpho.R<br> R scripts for data transformation of wing shape</p> <p>genital_morpho.R<br> R scripts for data transformation of genital morphology</p> <p><br> Models_used.R:<br> R scripts used to produce the statistical analyses.</p> <p>genital_morpho_master_with_pca.txt<br> Phenotypic data for genital morphology</p> <p>wing_contemporary_morpho_master_with_pca.txt<br> Phenotypic data for contemporary wing patterns</p> <p>wing_historic_morpho_master_with_pca.txt<br> Phenotypic data for wing patterns from museum samples</p> <p>The text files contains the following information:</p> <p>ID = Individual ID<br> genotyped_allopatric = was the individual genotyped<br> latitude<br> longitude<br> DATE = Date of collection<br> DAY = Day of collection<br> MONTH = Month of collection<br> YEAR = Year of collection<br> SPOT = Collection site<br> boxplotID = ID to reproduce boxplot order as used in the paper<br> colory = color code to plot<br> cycle = year cycle (2018/19 or 2020/21)<br> yeartype = even or odd year<br> genital_x_LM1 = linear measure of genital landmark 1 along the x axis<br> genital_y_LM1 = linear measure of genital landmark 1 along the y axis<br> genital_x_LM2 = linear measure of genital landmark 2 along the x axis <br> genital_y_LM2 = linear measure of genital landmark 2 along the y axis <br> genital_x_LM3 = linear measure of genital landmark 3 along the x axis <br> genital_y_LM3 = linear measure of genital landmark 3 along the y axis <br> genital_x_LM4 = linear measure of genital landmark 4 along the x axis <br> genital_y_LM4 = linear measure of genital landmark 4 along the y axis <br> genital_x_LM5 = linear measure of genital landmark 5 along the x axis <br> genital_y_LM5 = linear measure of genital landmark 5 along the y axis <br> v_t1 = length relationship between v and t1<br> v_t2 = length relationship between v and t2 <br> v_t3 = length relationship between v and t3 <br> t3_t1 = length relationship between t3_t1 <br> t3_t2 = length relationship between t3_t2 <br> t2_t1 = length relationship between t2_t1 <br> v_tg = length relationship between v and tg <br> PC1.x = PC1 axis for unprojected morphospace<br> PC2.x = PC2 axis for unprojected morphospace <br> PC3.x = PC3 axis for unprojected morphospace <br> PC4.x = PC4 axis for unprojected morphospace <br> PC5.x = PC5 axis for unprojected morphospace <br> PC6.x = PC6 axis for unprojected morphospace <br> PC7.x = PC7 axis for unprojected morphospace <br> PC1.y = PC1 axis for projected morphospace <br> PC2.y = PC2 axis for projected morphospace <br> PC3.y = PC3 axis for projected morphospace <br> PC4.y = PC4 axis for projected morphospace <br> PC5.y = PC5 axis for projected morphospace <br> PC6.y = PC6 axis for projected morphospace <br> PC7.y = PC7 axis for projected morphospace</p> <p> </p> <p><br> wing_ProcCoord1 = Procrustes coordinate 1<br> wing_ProcCoord2 = Procrustes coordinate 2<br> wing_ProcCoord3 = Procrustes coordinate 3<br> wing_ProcCoord4 = Procrustes coordinate 4<br> wing_ProcCoord5 = Procrustes coordinate 5<br> wing_ProcCoord6 = Procrustes coordinate 6<br> wing_ProcCoord7 = Procrustes coordinate 7<br> wing_ProcCoord8 = Procrustes coordinate 8<br> wing_ProcCoord9 = Procrustes coordinate 9<br> wing_ProcCoord10 = Procrustes coordinate 10<br> wing_ProcCoord11 = Procrustes coordinate 11<br> wing_ProcCoord12 = Procrustes coordinate 12<br> wing_ProcCoord13 = Procrustes coordinate 13<br> wing_ProcCoord14 = Procrustes coordinate 14<br> wing_ProcCoord15 = Procrustes coordinate 15<br> wing_ProcCoord16 = Procrustes coordinate 16<br> wing_ProcCoord17 = Procrustes coordinate 17<br> wing_ProcCoord18 = Procrustes coordinate 18<br> wing_ProcCoord19 = Procrustes coordinate 19<br> wing_ProcCoord20 = Procrustes coordinate 20<br> wing_ProcCoord21 = Procrustes coordinate 21<br> wing_ProcCoord22 = Procrustes coordinate 22<br> wing_ProcCoord23 = Procrustes coordinate 23<br> wing_ProcCoord24 = Procrustes coordinate 24<br> wing_ProcCoord25 = Procrustes coordinate 25<br> wing_ProcCoord26 = Procrustes coordinate 26<br> wing_ProcCoord27 = Procrustes coordinate 27<br> wing_ProcCoord28 = Procrustes coordinate 28<br> wing_ProcCoord29 = Procrustes coordinate 29<br> wing_ProcCoord30 = Procrustes coordinate 30<br> wing_ProcCoord31 = Procrustes coordinate 31<br> wing_ProcCoord32 = Procrustes coordinate 32<br> wing_ProcCoord33 = Procrustes coordinate 33<br> wing_ProcCoord34 = Procrustes coordinate 34<br> wing_ProcCoord35 = Procrustes coordinate 35<br> wing_ProcCoord36 = Procrustes coordinate 36<br> wing_ProcCoord37 = Procrustes coordinate 37<br> wing_ProcCoord38 = Procrustes coordinate 38<br> wing_ProcCoord39 = Procrustes coordinate 39<br> wing_ProcCoord40 = Procrustes coordinate 40<br> wing_ProcCoord41 = Procrustes coordinate 41<br> wing_ProcCoord42 = Procrustes coordinate 42<br> wing_ProcCoord43 = Procrustes coordinate 43<br> wing_ProcCoord44 = Procrustes coordinate 44<br> wing_ProcCoord45 = Procrustes coordinate 45<br> wing_ProcCoord46 = Procrustes coordinate 46<br> wing_ProcCoord47 = Procrustes coordinate 47<br> wing_ProcCoord48 = Procrustes coordinate 48<br> wing_ProcCoord49 = Procrustes coordinate 49<br> wing_ProcCoord50 = Procrustes coordinate 50<br> wing_ProcCoord51 = Procrustes coordinate 51<br> wing_ProcCoord52 = Procrustes coordinate 52<br> wing_ProcCoord53 = Procrustes coordinate 53<br> wing_ProcCoord54 = Procrustes coordinate 54<br> PC1.x = PC1 unprojected<br> PC2.x = PC2 unprojected<br> PC3.x = PC3 unprojected<br> PC4.x = PC4 unprojected<br> PC5.x = PC5 unprojected<br> PC6.x = PC6 unprojected<br> PC7.x = PC7 unprojected<br> PC8.x = PC8 unprojected<br> PC9.x = PC9 unprojected<br> PC10.x = PC10 unprojected<br> PC11.x = PC11 unprojected<br> PC12.x = PC12 unprojected<br> PC13.x = PC13 unprojected<br> PC14.x = PC14 unprojected<br> PC15.x = PC15 unprojected<br> PC16.x = PC16 unprojected<br> PC17.x = PC17 unprojected<br> PC18.x = PC18 unprojected<br> PC19.x = PC19 unprojected<br> PC20.x = PC20 unprojected<br> PC21.x = PC21 unprojected<br> PC22.x = PC22 unprojected<br> PC23.x = PC23 unprojected<br> PC24.x = PC24 unprojected<br> PC25.x = PC25 unprojected<br> PC26.x = PC26 unprojected<br> PC27.x = PC27 unprojected<br> PC28.x = PC28 unprojected<br> PC29.x = PC29 unprojected<br> PC30.x = PC30 unprojected<br> PC31.x = PC31 unprojected<br> PC32.x = PC32 unprojected<br> PC33.x = PC33 unprojected<br> PC34.x = PC34 unprojected<br> PC35.x = PC35 unprojected<br> PC36 = PC36 unprojected<br> PC37 = PC37 unprojected<br> PC38 = PC38 unprojected<br> PC39 = PC39 unprojected<br> PC40 = PC40 unprojected<br> PC41 = PC41 unprojected<br> PC42 = PC42 unprojected<br> PC43 = PC43 unprojected<br> PC44 = PC44 unprojected<br> PC45 = PC45 unprojected<br> PC46 = PC46 unprojected<br> PC47 = PC47 unprojected<br> PC48 = PC48 unprojected<br> PC49 = PC49 unprojected<br> PC50 = PC50 unprojected<br> PC51 = PC51 unprojected<br> PC52 = PC52 unprojected<br> PC53 = PC53 unprojected<br> PC54 = PC54 unprojected<br> PC1.y = PC1 projected<br> PC2.y = PC2 projected<br> PC3.y = PC3 projected<br> PC4.y = PC4 projected<br> PC5.y = PC5 projected<br> PC6.y = PC6 projected<br> PC7.y = PC7 projected<br> PC8.y = PC8 projected<br> PC9.y = PC9 projected<br> PC10.y = PC10 projected<br> PC11.y = PC11 projected<br> PC12.y = PC12 projected<br> PC13.y = PC13 projected<br> PC14.y = PC14 projected<br> PC15.y = PC15 projected<br> PC16.y = PC16 projected<br> PC17.y = PC17 projected<br> PC18.y = PC18 projected<br> PC19.y = PC19 projected<br> PC20.y = PC20 projected<br> PC21.y = PC21 projected<br> PC22.y = PC22 projected<br> PC23.y = PC23 projected<br> PC24.y = PC24 projected<br> PC25.y = PC25 projected<br> PC26.y = PC26 projected<br> PC27.y = PC27 projected<br> PC28.y = PC28 projected<br> PC29.y = PC29 projected<br> PC30.y = PC30 projected<br> PC31.y = PC31 projected<br> PC32.y = PC32 projected<br> PC33.y = PC33 projected<br> PC34.y = PC34 projected<br> PC35.y = PC35 projected</p> <p> </p> <p> </p> <p> </p> <p>Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795):</p> <p>all_euryale_calls.vcf.gz<br> The unfiltered VCF file</p> <p>euryale_V2.sh<br> Shell script for the genomic data analysis</p> <p>introgress.R<br> R script for running Introgress</p> <p>introgress_all_east2.txt<br> Output of Introgress for the Eastern contact zone</p> <p>introgress_all_west2.txt<br> Output of Introgress for the Western contact zone</p> <p>Admixture_output.txt<br> Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID</p> <p>Outliers2BombyxMori.txt<br> BLAST summary of outlier regions against Bombyx Mori</p> <p>Outliers2ManjolaJurtina.txt<br> BLAST summary of outlier regions against Manjola jurtina</p> <p>Outliers2ParargeAegeria.txt<br> BLAST summary of outlier regions against Pararge aegeria</p> <p> </p>
Delta smelt (Hypomesus transpacificus) life cycle model input data.
Synthesized data used for fitting delta smelt population dynamics models, essentially consisting of predictor variables (environmental conditions and indices of prey and predators) and response variables (abundance indices). Input data is sourced from a variety of both federal and California state government monitoring programs taking place within the San Francisco Estuary, California. These include California Department of Fish and Wildlife fish surveys, Interagency Ecological Program's Environmental Monitoring Program for zooplankton, California Department of Water Resources' Dayflow, and United States Geological Survey water monitoring data. The sourced data are recorded from sub-hourly to monthly time scales and at various spatial scales, aggregated at monthly or greater time scales using summary statistics (e.g. means) and are not spatially explicit but use spatial stratification approaches for statistic calculation as appropriate.
Hydraulic Constraints on Two Life History Stages of Larrea tridentata in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (2002-2003)
Maintaining high rates of water loss during times of high resource availability could allow establishing woody desert perennials to grow quickly by allowing them to take advantage of the fleeting but abundant monsoonal moisture typical of warm deserts like the Chihuahuan. However, a plant cannot endlessly increase water loss in order to grow faster --there are hydraulic constraints on rates of water loss. The hydraulic properties of each particular plant xylem and soil microsite, as well as the AR:AL absorbing root area to transpiring leaf area ratio) interact to set limits on rates of water loss. If transpiration rates become too high, cavitation may limit the ability of the xylem to supply water to the leaves. The main objective of this study was to test two hypotheses on a population of Larrea tridentata at the Sevilleta LTER in central New Mexico (1) do small plants grow faster and use water less conservatively than large, and (2) are there differences in the hydraulic constraints on small and large plants. Measurements were made every six weeks in the spring, summer and fall from April 2002 - August 2003. Field measurements of shoot growth, gas exchange and plant and soil water potentials were made to determine growth rates and water use. Measurements of leaf specific conductance determined the ability of the xylem to supply water to the leaves. Excavation findings were used to estimate (AR:AL). Xylem vulnerability curves and soil texture analysis were used to determine the hydraulic properties of the plant xylem and soil. A model determined where the limiting conductance occurred in the plant-soil continuum.
Figure 2. UCMS Bibliographic Record Life Cycle
<p><strong>Records identification process</strong></p> <p>Immediately after the completion of the preliminary activities for those records that change to the «In union» state, the matching-records algorithm will be run, which will result in the final records of HLUC (master records and single-entry records). The way the records will be merged depends on the use for which these records (master and/or single records) are intended, as shown in Figure 5. Specifically, there is a different way that the records will be merged in order to be presented through the Online Public Access Catalogue, or for the Interlibrary Loan System and they will be structured differently for display in the Cataloguing Center. The differences are mainly about the way and the degree of the field merging</p>
Fig. 2 in Growth and reproduction in captivity unveils remarkable life-history plasticity in the smallnose fanskate, Sympterygia bonapartii (Chondrichthyes: Rajiformes)
Fig. 2. Egg cases and neonate of Sympterygia bonapartii born at Temaikèn Aquarium (Argentina). ah, anterior horns; mt, mucous tendrils; ph, posterior horns. Scale bar: 20 mm.
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</p>
Retrieving Affected Versions by Leveraging the Life Cycle of Defects
<p>This is the online appendix for our paper submission entitled "Retrieving Affected Versions by Leveraging the Life Cycle of Defects"</p>
Figure 2 in Life table parameters of Tetranychus urticae (Trombidiformes: Tetranychidae) on four strawberry cultivars
Figure 2. Age-specific survival rate (lx), age-stage fecundity of female (fxj) and age-specific fecundity rate (mx) of Tetranychus urticae on four strawberry cultivars.
Figure 4 in Life table parameters of Tetranychus urticae (Trombidiformes: Tetranychidae) on four strawberry cultivars
Figure 4. Age-specific survivorship (lx), and fecundity (mx) of Tetranychus urticae on four strawberry cultivars.
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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.