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Using stable isotopes to link biogeochemical processes to biodiversity of conservation concern
<b>Description: </b><p>This data was collected at the SAFE experimental area, and two primary forest sites within the greater SAFE landscape: the Danum Valley conservation area (DVCA; N4.962°, E117.689°) and Maliau Basin conservation area (MBCA; N4.853°, E116.844°), located 36 Km north and 69 Km west of the SAFE project, respectively. DVCA is a 438 Km2 lowland dipterocarp forest reserve close to the Segama River at 210 m elevation. In comparison, MBCA is a 588 Km2 lowland and hill dipterocarp forest reserve, close to the Maliau River and reaching 1, 675 m at the highest point. Both are afforded the highest status as Class I Protected Forest Reserves by the Sabah state government, owing to the value of the areas for conservation and education. <br>In 2015 we sampled all six SAFE sites. In 2016 we did not resample one of the repeatedly logged-forest sites ('F') for logistical reasons, but extended sampling to include primary forest sites Maliau and Danum. In each year, we performed ten nights of trapping using six four-bank harp traps set along existing trails at each site (total: 60 harp trap nights per site). We checked traps late at night and early the next morning (20.30 and 08.30), and moved them each day by at least 20m. For logistical reasons, site F was only sampled using 30 harp trap nights in 2015, and site LFE was sampled three times in 2015 due to low capture rates.<br>Upon capture, we identified each bat to species and, for adult individuals, a wing membrane biopsy was taken using a 3mm punch (Schuco, Walford, UK) for stable isotope analyses and placed in separate tubes for same day processing. Heavily pregnant, highly stressed or recently recaptured bats were not sampled.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/11"><b>Using stable isotopes to link biogeochemical processes to biodiversity of conservation concern</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Research grant, NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 (374) )</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 JLD.4 (41))</li><li>Sabah Biodiversity Centre (SABC) (Research licence JKM.1000-2/2 JLD.5 (153))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5113429">here</a></p><p><b>Files: </b>This consists of 1 file: 1_Kemp_bat_iso_data.xlsx</p><p><b>1_Kemp_bat_iso_data.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Carbon and nitrogen natural abundance stable isotope data of insects</b> (described in worksheet Insect_isotope)</p><p>Description: A single year dataset of carbon and nitrogen stable isotope values on volant insects across a gradient of logging disturbance in Sabah, Malaysian Borneo. The data was collected between May – July 2017. </p><p>Number of fields: 15</p><p>Number of data rows: 173</p><p>Fields: </p><ul><li><b>Order</b>: Taxonomic order (Field type: id)</li><li><b>Family</b>: Taxonomic family (Field type: id)</li><li><b>Taxa</b>: Highest taxonomic classification (Field type: taxa)</li><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Site</b>: Sample site (Field type: id)</li><li><b>Plot</b>: Vegetation plot (2 sampled within each site) (Field type: id)</li><li><b>Bottle</b>: Either "top" or "bottom" bottle of the Malaise trap (Field type: categorical)</li><li><b>Feeding_Guild</b>: Feeding Guild (PHY = phytophagous; DET = detritivorous, and PRED = predatory) (Field type: categorical trait)</li><li><b>Weight</b>: Sample weight (Field type: numeric trait)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak (uA), i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight (Field type: numeric trait)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric trait)</li><li><b>X15N</b>: The delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric trait)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric trait)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric trait)</li><li><b>X13C</b>: The delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric trait)</li></ul></li><li><p><b>Georeferenced bat capture data from three field seasons, 2015-2017 in Sabah, Malaysian Borneo</b> (described in worksheet Bat_data)</p><p>Description: This dataset provides capture data from the SAFE experimental area as well as Danum Valley and Maliau Basin. The dataset covers three field campaigns spanning from 2015 – 2017. The dataset includes species annotation, capture date and time; capture location (latitude and longitude); sex, age; forearm and weight measurement, and reproductive condition. There is also an identifier to match captured individuals with isotope values.</p><p>Number of fields: 19</p><p>Number of data rows: 2490</p><p>Fields: </p><ul><li><b>Bat_no</b>: Unique identifier for each individual bat captured (Field type: id)</li><li><b>Day</b>: The day that individual was captured (Field type: id)</li><li><b>Month</b>: The month that individual was captured (Field type: id)</li><li><b>Year</b>: The year that individual was captured (Field type: id)</li><li><b>Date</b>: Date individual was captured (Field type: date)</li><li><b>TrapName</b>: Unique identifier for each trap which was erected (trap position within sites were not consistent between years, largely due to changes in the trails and vegetation structure) (Field type: location)</li><li><b>Lat</b>: Latitude of the trap in which the bat was captured (Field type: latitude)</li><li><b>Long</b>: Longitude of the trap in which the bat was captured (Field type: longitude)</li><li><b>Site</b>: The site at which the capture was made (Field type: id)</li><li><b>Family</b>: Family classification (Field type: categorical trait)</li><li><b>Species</b>: Species classification (Field type: taxa)</li><li><b>Sex</b>: The sex of each capture (M= Male; F = Female) (Field type: categorical trait)</li><li><b>Age</b>: The age of each capture (A = Adult; J = Juvenile ()) (Field type: categorical trait)</li><li><b>Forearm</b>: Forearm measurement (Field type: numeric trait)</li><li><b>Weight</b>: Weight of bat (Field type: numeric trait)</li><li><b>Reproductive_condition</b>: Reproductive condition of the individual (M= Male; NR = Non reproductive, i.e. not yet born young; L = Lactating, i.e. milk visible below the teet; P = Pregnant; PL = Post-lactating, i.e. not currently pregnant or lactating, but evidence of having born young from distended nipples. (Field type: categorical trait)</li><li><b>Time</b>: Whether the individual was captured PM (traps checked for fly-ins between dusk and ~ 19:30) or AM (traps checked for fly-ins between time of evening check and ~ 08:30) (Field type: categorical trait)</li><li><b>Guild</b>: Echolocation guilds with calls classified as HDC = High-duty-cycle or LDC = Low-duty-cycle (Field type: categorical trait)</li><li><b>Biopsy</b>: Unique identifier to associate the captured individual with the isotope data derived from the wing membrane biopsy. If no biopsy was taken there is a reason given: "RECAPTURE" – recapture as denoted by a fresh biopsy hole; "ESCAPED" – escaped the trap before handling; "BLEEDING" – with any sign of bleeding we avoided handling; "STRESSED" – due to ants in the trap, larger bats in the trap etc.; "NOT HEALED" – biopsy wound not healed from the previous visits ~4 weeks prior; "DEAD" – dead from stress; ants, or extreme weather; "PREGNANT" – heavily pregnant, therefore we avoided handling; "WITH YOUNG" – adult female with young attached; "NO BIOPSY PEN" – no sterile biopsy pens available in the field; "LOST" – biopsy lost after having been taken ; "RELEASED" – due to some kind of stress; "NOT TAKEN" - could be any of the above or another reason. (Field type: id)</li></ul></li><li><p><b>Carbon and nitrogen natural abundance stable isotope data of bat wing tissue, sampled across three field seasons, 2015-2017 in Sabah, Malaysian Borneo.</b> (described in worksheet Bat_isotope)</p><p>Description: This dataset provides carbon and nitrogen stable isotope values of bat wing tissue (3mm punch) from individuals sampled across a gradient of logging disturbance in Sabah, Malaysian Borneo. The data was collected over three discrete field seasons, February – May 2015, March - July 2016 and May – July 2017</p><p>Number of fields: 10</p><p>Number of data rows: 3145</p><p>Fields: </p><ul><li><b>N</b>: Number to show the order in which the samples were run (currently the sheet is ordered in distinct runs (32 in total across the 3 years) (Field type: id)</li><li><b>Name</b>: Unique identifier to associate the isotope data with the captured individuals (matched to "Biopsy" column in "BatData.csv"). (Field type: id)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>N_weight</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>X15N</b>: The delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.1</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight (Field type: numeric)</li><li><b>C_weight</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>X13C</b>: The delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li><li><b>Year</b>: Year of sampling (Field type: id)</li></ul></li><li><p><b>Carbon and nitrogen natural abundance stable isotope data of basal resources (living leaves, leaf litter, soil, algae and dead wood), sampled across three field seasons, 2015-2017 in Sabah, Malaysian Borneo.</b> (described in worksheet Basal_resource_data)</p><p>Description: This dataset provides carbon and nitrogen stable isotope values of key basal resources representing the primary and detrital resource bases. The data was collected over three discrete field seasons, February – May 2015, March - July 2016 and May – July 2017.</p><p>Number of fields: 14</p><p>Number of data rows: 647</p><p>Fields: </p><ul><li><b>Source</b>: The type of biological material (Field type: id)</li><li><b>Name</b>: Identifier for each samples, of the format. PlotID_ReplicateNo (Field type: id)</li><li><b>Site</b>: Sample site (Field type: id)</li><li><b>Plot</b>: Vegetation plot (2 sampled within each site) (Field type: id)</li><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Round</b>: In some years some sites were visited twice (at least 1 month between visits) (Field type: replicate)</li><li><b>Weight</b>: Sample weight (Field type: numeric)</li><li><b>Beam.Area.N</b>: Measure of the nitrogen peak i.e. calculated as the area under the nitrogen curve by Calisto software. This value is directly related to N_weight. (Field type: numeric)</li><li><b>ugN</b>: Measure of the elemental nitrogen content of the sample (Field type: numeric)</li><li><b>X15N</b>: The delta value of the sample, which describes the ration of 15N: 14N isotopes (Field type: numeric)</li><li><b>Beam.Area.C</b>: Measure of the carbon peak i.e. calculated as the area under the carbon curve by Calisto software. This value is directly related to C_weight. (Field type: numeric)</li><li><b>ugC</b>: Measure of the elemental carbon content of the sample (Field type: numeric)</li><li><b>X13C</b>: The delta value of the sample, which describes the ration of 13C: 12C isotopes (Field type: numeric)</li><li><b>Year</b>: Year in which the sample was collected (Field type: id)</li></ul></li></ol><p><b>Date range: </b>2015-02-16 to 2017-07-21</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros bicolor</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros dyacorum</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br> -  -  -  -  -  Nycteridae <br> -  -  -  -  -  -  <i>Nycteris</i> <br> -  -  -  -  -  -  -  <i>Nycteris tragata</i> <br> -  -  -  -  -  Emballonuridae <br> -  -  -  -  -  -  <i>Emballonura</i> <br> -  -  -  -  -  -  -  <i>Emballonura alecto</i> <br> -  -  -  -  -  -  -  <i>Emballonura monticola</i> <br> -  -  -  -  -  Vespertilionidae <br> -  -  -  -  -  -  <i>Kerivoula</i> <br> -  -  -  -  -  -  -  <i>Kerivoula hardwickii</i> <br> -  -  -  -  -  -  -  <i>Kerivoula intermedia</i> <br> -  -  -  -  -  -  -  <i>Kerivoula lenis</i> <br> -  -  -  -  -  -  -  <i>Kerivoula minuta</i> <br> -  -  -  -  -  -  -  <i>Kerivoula papillosa</i> <br> -  -  -  -  -  -  -  <i>Kerivoula pellucida</i> <br> -  -  -  -  -  -  -  <i>Kerivoula whiteheadi</i> <br> -  -  -  -  -  -  <i>Murina</i> <br> -  -  -  -  -  -  -  <i>Murina aenea</i> <br> -  -  -  -  -  -  -  <i>Murina cyclotis</i> <br> -  -  -  -  -  -  -  <i>Murina peninsularis</i> <br> -  -  -  -  -  -  -  <i>Murina rozendaali</i> <br> -  -  -  -  -  -  -  <i>Murina suilla</i> <br> -  -  -  -  -  -  <i>Myotis</i> <br> -  -  -  -  -  -  -  <i>Myotis muricola</i> <br> -  -  -  -  -  -  -  <i>Myotis ridleyi</i> <br> -  -  -  -  -  -  <i>Pipistrellus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus javanicus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus tenuis</i> <br> -  -  -  -  -  -  <i>Pipistrellus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus javanicus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus tenuis</i> <br> -  -  -  -  -  -  <i>Philetor</i> <br> -  -  -  -  -  -  -  <i>Philetor brachypterus</i> <br> -  -  -  -  -  -  <i>Scotophilus</i> <br> -  -  -  -  -  -  -  <i>Scotophilus kuhlii</i> <br> -  -  -  -  -  -  <i>Harpiocephalus</i> <br> -  -  -  -  -  -  -  <i>Harpiocephalus harpia</i> <br> -  -  -  -  -  -  <i>Phoniscus</i> <br> -  -  -  -  -  -  -  <i>Phoniscus atrox</i> <br> -  -  -  -  -  -  <i>Hesperoptenus</i> <br> -  -  -  -  -  -  -  <i>Hesperoptenus blanfordi</i> <br> -  -  -  -  -  Pteropodidae <br> -  -  -  -  -  -  <i>Balionycteris</i> <br> -  -  -  -  -  -  -  <i>Balionycteris maculata</i> <br> -  -  -  -  -  Megadermatidae <br> -  -  -  -  -  -  <i>Megaderma</i> <br> -  -  -  -  -  -  -  <i>Megaderma spasma</i> <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus affinis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus creaghi</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Blattodea <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Lepidoptera <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Psocodea <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Trichoptera <br> -  -  -  -  Diptera <br> -  -  -  -  -  Mycetophilidae <br> -  -  -  -  -  Empididae <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  Insecta <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Blattodea <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Lepidoptera <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Psocodea <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Trichoptera <br> -  -  -  -  Diptera <br> -  -  -  -  -  Mycetophilidae <br> -  -  -  -  -  Empididae <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  Insecta <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Blattodea <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Lepidoptera <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  -  Psocodea <br> -  -  -  -  Coleoptera <br> -  -  -  -  -  Endomychidae <br> -  -  -  -  -  Bostrichidae <br> -  -  -  -  -  Chrysomelidae <br> -  -  -  -  -  Erotylidae <br> -  -  -  -  -  Carabidae <br> -  -  -  -  -  Lycidae <br> -  -  -  -  -  Coccinellidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Nitidulidae <br> -  -  -  -  -  Lucanidae <br> -  -  -  -  -  Ptilodactylidae <br> -  -  -  -  -  Anthribidae <br> -  -  -  -  -  Staphylinidae <br> -  -  -  -  -  Scarabaeidae <br> -  -  -  -  -  -  Melolonthinae <br> -  -  -  -  -  -  Ruteliinae <br> -  -  -  -  -  Cantharidae <br> -  -  -  -  -  Mordellidae <br> -  -  -  -  -  Rhipiceridae <br> -  -  -  -  -  Cleridae <br> -  -  -  -  -  Ptinidae <br> -  -  -  -  -  Cerambycidae <br> -  -  -  -  -  Throscidae <br> -  -  -  -  -  Curculionidae <br> -  -  -  -  -  Histeridae <br> -  -  -  -  -  Cerylonidae <br> -  -  -  -  -  -  <i>Gyrelon</i> <br> -  -  -  -  -  Melolonthidae <br> -  -  -  -  -  -  <i>Apogonia</i> <br> -  -  -  -  Hemiptera <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Cicadellidae <br> -  -  -  -  -  Cydnidae <br> -  -  -  -  -  Coreidae <br> -  -  -  -  -  Fulgoroidea <br> -  -  -  -  -  Reduviidae <br> -  -  -  -  -  Miridae <br> -  -  -  -  Trichoptera <br> -  -  -  -  Diptera <br> -  -  -  -  -  Mycetophilidae <br> -  -  -  -  -  Empididae <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Ichneumoidea <br> -  -  -  -  -  Vespidae <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  Bethylidae <br> -  -  -  -  -  Apoidea <br> -  -  -  -  -  Braconidae <br> -  -  -  -  -  Pompilidae <br> -  -  -  -  -  Scoliidae <br> -  -  -  -  -  Chalcidoidea <br> -  -  -  Arachnida <br> -  -  -  -  Araneae <br></div><p></p>
Pre-Processed Cancer Multi-Omic Data from TCGA and Synthetic Data
<p><strong>ABSTRACT </strong></p> <p>It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. </p> <p>In addition, we provide synthetic data for a mixture of isotropic distributions.</p> <p><strong>Instructions: </strong></p> <p>Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients.</p> <p>The summary data contains only the numerical values. The columns are the features and the rows are the observations.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090.</p> <p>https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Dataset - An Analytical Hierarchy Process (AHP) model for mapping expectations
<p>This dataset contains detailed information about an Analytical Hierarchy Process (AHP) model developed in order to map actors' perceptions about their contributions to a gender policy. </p>
Pre-processed IgH repertoire sequencing data from BioProject PRJNA748239
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p>software_versions pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p>quality_thresholds FilterSeq.py pRESTO Q>20</p> <p>paired_reads_assembly AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p>primer_match_cutoffs MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p>consensus_building BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p>collapsing_method CollapseSeq.py pRESTO</p> <p>germline_database IMGT</p> <p> </p> <p>Format</p> <p> </p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>C_CALL</strong> Isotype subclass</p> <p><strong>SEQUENCE_ID</strong> Sequence identifier</p> <p><strong>V_CALL</strong> V segment gene and allele</p> <p><strong>D_CALL</strong> D segment gene and allele</p> <p><strong>J_CALL</strong> J segment gene and allele</p> <p><strong>JUNCTION_LENGTH</strong> Junction length</p> <p><strong>CONSCOUNT</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT</strong> UMI count for the given unique sequence</p> <p><strong>ISOTYPE</strong> Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R</strong> Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S</strong> Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R</strong> Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S</strong> Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL</strong> Total number of mutations in V gene </p> <p><strong>NP_LENGTH</strong> Total number of N and P additions<strong> </strong></p> <p><strong>SEQUENCE_INPUT</strong> Full length sequence</p> <p><strong>SEQUENCE_IMGT</strong> Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ</strong> position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION</strong> Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK</strong> IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>CDR3_AA_GRAVY</strong> CDR3 hydrophobicity</p> <p><strong>CDR3_AA_BULK</strong> CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC</strong> Normalized aliphatic index</p> <p><strong>CDR3_AA_POLARITY</strong> CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE</strong> normalised net charge</p> <p><strong>CDR3_AA_BASIC</strong> Basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC</strong> Acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC</strong> aromatic side chain conten</p> <p><strong>Subset</strong> Defined B cell subset </p> <p><strong>Repertoire</strong> Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR</strong> R/S ratio in CDR region</p> <p><strong>R_SFWR</strong> R/S ratio in FWR region</p> <p><strong>V_GENE</strong> V segment gene</p> <p><strong>D_GENE</strong> D segment gene</p> <p><strong>J_GENE</strong> J segment gene</p> <p><strong>V_FAM</strong> V family gene</p> <p><strong>Run</strong> ID of sequencing run</p> <p><strong>Sex</strong> Sex of the Subject</p> <p><strong>Age</strong> Age of the subject</p> <p><strong>UNIQUE_ID</strong> Subject identifier </p> <p><strong>SAMPLE</strong> Sample identifier, linking back to raw data</p> <p><strong>Bcellno</strong> Number of input B cells</p> <p><strong>Cells</strong> Cell type </p> <p> </p> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
Chemical Process Technologies: Continuous Stirred Tank Reactor
<p>Overview of the continuously stirred tank reactor (CSTR) in chemical processing</p>
Chemical Process Technologies: Batch Reactors
<p>An overview of the fundamentals of batch technology in chemical processing.</p>
Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
<p>Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characteristics. In this study, we compiled global datasets of lake gross primary production (GPP), ecosystem respiration (ER), and zooplankton allochthony to compare the strength and shape of relationships with physicochemical characteristics across a broad set of lakes. GPP was positively related to total phosphorus (TP) in lakes with intermediate TP concentrations (11 - 75 μg L<sup>-1</sup>) and was highest in lakes with intermediate dissolved organic carbon (DOC) concentrations. While ER and GPP were strongly positively correlated, decoupling occurred at high DOC concentrations. Lastly, allochthony had a unimodal relationship with TP and related variably to DOC. By integrating metabolism and allochthony, we identified similar change points in GPP and zooplankton allochthony at intermediate DOC (4.5 - 10 mg L<sup>-1</sup>) and TP (8 - 20 μg L<sup>-1</sup>) concentrations, indicating that allochthony and GPP may be coupled and inversely related. The ratio of DOC : nutrients also helped to identify conditions where lake organic matter processing responded more to autochthonous or allochthonous organic matter sources. As lakes globally face eutrophication and browning, predicting how lake organic matter processing will respond requires an updated paradigm that incorporates nonlinear dynamics and interactions.</p>
Toy datasets for GTN materials about Metabolomics data processing
<p>These are the datasets needed for the GTN tutorial about Data processing of Metabolomics MS-based data.</p> <p>The dataMatrix and corresponding sampleMetadata files are simulated data, specially created from scratch for the GTN tutorial.</p> <p>The variableMetadata file's columns of m/z and rt characteristics have been filled with values picked from a table obtained by an extraction process of some untargetted MS raw files. MS raw files used are from https://zenodo.org/record/3757956; extraction was performed using XCMS with parameters that might not be optimal.</p>
Integrated automatic design process for robot swarms
<p>Demonstration of the Integrated automatic process for robot swarms in three missions: Aggregation, Foraging, and Migration.<br> The dataset contains the following:<br> 1. SML (Swarm Mission Language) related files: <br> - specification files used to create missions,<br> - generated files to be used by an optimization method <br> 2. AUTOMODE related files:<br> - the log files running AUTOMODE - an optimization method that generates control software for different missions<br> - generated control software <br> 3. Demonstration:<br> - snapshots and videos of running the missions on real robots</p>
Unveiling the Patterns of Reticulated Evolutionary Processes with Phylogenomics: Hybridization and Polyploidy in the genus Rosa
<p>Reticulation, caused by hybridization and allopolyploidization, is considered an important and frequent phenomenon in the evolution of numerous plant lineages. Although both processes represent important driving forces of evolution, they are mostly ignored in phylogenetic studies involving a large number of species. Indeed only a scattering of methods exists to recover a comprehensive reticulated evolutionary history for a broad taxon sampling. Plastid markers, sometimes combined with a few nuclear sequences, are therefore favored, even though they restrict in-depth studies of hybridization and polyploidization. The genus <i>Rosa</i> encompasses c. 150 species widely distributed throughout the northern hemisphere and represents a challenging taxonomic group in which hybridization and polyploidization are prominent. Our main objective was to develop a general framework that would take patterns of reticulation into account in the study of the phylogenetic relationships among <i>Rosa</i> species. Using amplicon sequencing we targeted allele variation in the nuclear genome as well as haploid sequences in the chloroplast genome. A stepwise diploids-first approach was developed to reconstruct the reticulate evolutionary history of the genus <i>Rosa</i>. We successfully recovered robust plastid and nuclear phylogenies and performed in-depth tests for several scenarios of hybridization. Using this strategy, we were able to resolve most of the evolutionary relationships among <i>Rosa</i> subgenera, sections, and selected species, and we provide new directions for a future revision of the infrageneric classification in <i>Rosa</i>. The stepwise strategy proposed here can be used to reconstruct the phylogenetic relationships of other challenging taxonomic groups with large numbers of hybrid and polyploid taxa.</p>
DNA sequences of transgenes detected via environmental DNA (raw ABI files, processed FASTA files, and reference alignments)
We demonstrate that simple, non-invasive environmental DNA (eDNA) methods can detect transgenes of genetically modified (GM) animals from terrestrial and aquatic sources in invertebrate and vertebrate systems. We detected transgenic fragments between 82-234 bp through targeted PCR amplification of environmental DNA extracted from food media of GM fruit flies (<i>Drosophila melanogaster</i>), feces, urine, and saliva of GM laboratory mice (<i>Mus musculus</i>), and aquarium water of GM tetra fish (<i>Gymnocorymbus ternetzi</i>). With rapidly growing accessibility of genome-editing technologies such as CRISPR, the prevalence and diversity of GM animals will increase dramatically. GM animals have already been released into the wild with more releases planned in the future. eDNA methods have the potential to address the critical need for sensitive, accurate, and cost-effective detection and monitoring of GM animals and their transgenes in nature.
Results of the Web-Delphi process to YLV stakeholders (three rounds)
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3 (Testing the framework with empirical applications), Results of the Web-Delphi process to TLV stakeholders (three rounds), about the views of stakeholders regarding “The relevance of the following value aspects for the evaluation of new medicines in this disease context is” (2020)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3, Deliverable 7.3 (Testing the framework with empirical applications), Testing the IMPACT-HTA Value Framework in collaboration with HTA agencies: Case studies on Non-Small Cell Lung Cancer and Spinal Muscular Atrophy (2021), Aris Angelis (LSHTM, LSE), Mónica Oliveira (IST), Teresa Rodrigues (IST), Liliana Freitas (IST), Carlos Bana e Costa (IST), Panos Kanavos (LSE)</p>
Text-fig. 19. Carnivore chewing traces on the olecranon process of ulna 98-599-B. in Consumption Of Canid Meat At The Gravettian Předmostí Site, The Czech Republic
Text-fig. 19. Carnivore chewing traces on the olecranon process of ulna 98-599-B.
Processed data for the "Property-Based Testing of Web APIs" paper
<p>Processed data for the "Property-Based Testing of Web APIs" paper. Each directory in the archive consists of:</p> <p>- metadata.json. Metadata about a test run - tested fuzzer name, run duration, etc</p> <p>- fuzzer.json - Structured fuzzer output</p> <p>- deduplicated_cases.json - Deduplicated reported failures, when fuzzers provide it</p> <p>- sentry.json - Cleaned Sentry events for this run</p> <p>- target.json - Parsed stdout for Gitlab & Disease.sh targets that were tested without Sentry integration</p>
16S processed data and shell processing scripts for "Epithelial-myeloid exchange of MHCII constrains immunity and microbiota composition"
<p>Main repo for MHCII on IECs 16S processing code.</p>
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
Sugar and nitrogen digestive processing does not explain the specialized relationship between euphonias and low quality fruits
<p>In the Neotropical region, euphonias (Euphonia spp., Fringillidae) are the quintessential example of specialized bird frugivores, making the bulk of feeding visits to certain mistletoes (Phoradendron spp., Santalaceae) and epiphytes in the genus Rhipsalis (Cactaceae), whose fruits have high water and low sugar and protein concentrations. Surprisingly, a mechanistic explanation for such specialized, otherwise rare, relationships is lacking. Using captive birds and artificial diets, we contrasted euphonias with frugivorous tanagers in the genus Thraupis (Thraupidae), which rarely eats Rhipsalis fruits, to test the hypothesis that the digestive capacity of euphonias entails them to exploit such low-energy fruits. We expected that compensatory feeding in response to decreasing energy density would occur only in euphonias, whose higher reliance on fruits would entail a lower nitrogen requirement than the tanagers. Euphonias and tanagers were both able to compensate energy intake as sugar density decreased, and both species had the same mass-corrected energy intake at any given sugar concentration. Similarly, euphonias and tanagers did not differ in mass-corrected maintenance nitrogen requirement. Therefore, the physiological traits we investigated do not explain euphonia´s specialization on Rhipsalis fruits. The fast rates of fruit passage typical of specialized avian frugivores as euphonias that entail the processing of a large volume of fruits, and the putative better abilities of such birds to deal with secondary compounds likely present in Rhipsalis fruits are other possible mechanisms that should be considered in future studies to unveil the mechanisms underlying the intriguing specialized relationships between euphonias and certain fruits.</p>
E-scooters: competition with shared bicycles and relationship to public transport (processed datasets)
<p>Processed datasets for article "E-scooters: competition with shared bicycles and relationship to public transport" by Łukasz Nawaro (University of Warsaw, Faculty of Economic Sciences).</p>
Open-Source Software Product Line Extraction Processes: the ArgoUML-SPL and Phaser Cases
<p>Collection of datasets and analysis scripts supporting the information provided in the text.</p> <p>There are two compressed files, one for the ArgoUML data and one for the Phaser data. Each compressed file contains a README describing important information.</p>
How hydrological connectivity regulates the plant recovery process in salt marshes
<p>1. Designing effective restoration strategies is a priority in recovering salt marsh plants. As a main driver underpinning the success of plant recovery process, hydrological connectivity can regulate life history process-based restoration strategies, but the relations are not clear.</p> <p>2. Plant recovering needs to go through a whole life history process, from seed to adult. Common restoration strategies are seed addition (SA) or seedling transplantation (ST), which start from seed germination and seedling growth stage. Besides these two strategies, another strategy starting from seed retention stage, microtopographic adjustment (MA), were designed to study the relationship with hydrological connectivity. And a framework was construct to assess a gradient of hydrological connectivity between marsh plain and sea and conducted several field experiments to test their relationships.</p> <p>3. The composite measurement of hydrological connectivity with five geomorphic variables can well represent the variation of environmental factors. Soil moisture, inundation frequency and sediment deposition were positive correlated, while soil salinity and hardness were negative correlated with hydrological connectivity.</p> <p>4. The success of different restoration strategies varied with hydrological connectivity. MA showed a monotone decreasing trend, while SA and ST showed unimodal trend with the increasing of hydrological connectivity. The important is, each strategy occupies a non-overlapping optimum range along hydrological connectivity gradient, they are low hydrological connectivity for MA (0 – 0.28), middle hydrological connectivity for SA (0.28 – 0.55) and high hydrological connectivity for ST (0.55 – 1).</p> <p>5. Synthesis and applications. Our findings expand the quantification of the hydrological environment beyond elevation or distance or other single index to include a range of elements of hydrological connectivity, and illustrate the underlying mechanisms of hydrological connectivity regulating restoration strategies based on different life stages. The results not only provide a reliable framework to assess hydrological connectivity, but also the guidance to select optimum restoration strategy under different hydrological connectivities, or to regulate the hydrological connectivity variables (topography on marsh plain and morphology of tidal creeks) to relief stresses. These findings will benefit ecological restoration and coastal management a lot.</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.