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799 results for “abundance data”
Data from: Empirical phylogenies and species abundance distributions are consistent with pre-equilibrium dynamics of neutral community models with gene flow
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Data from: Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore
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Data from: The abundance of deleterious polymorphisms in humans
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Data from: Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival
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Data from: Chronic Trichuris muris Infection Decreases Diversity of the Intestinal Microbiota and Concomitantly Increases the Abundance of Lactobacilli
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Data from: Host diet mediates a negative relationship between abundance and diversity of Drosophila gut microbiota
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Transcript abundance data from seedlings of wild-type Ws and ged1 (greening after extended darkness 1) mutant
GEO Series GSE12401. Arabidopsis thaliana. 6 samples. Type: Expression profiling by array.
Expression data from Dmp1-Cre targeted and non-targeted Cxcl12-abundant reticular (CAR) cells in mouse bone marrow
GEO Series GSE81399. Mus musculus. 6 samples. Type: Expression profiling by array.
Data from: Declining abundance of beetles, moths and caddisflies in the Netherlands
<p>Data used in Hallmann CA, Zeegers T, van Klink R, Vermeulen R, van Wielink P, Spijkers H, van Deijk J, van Steenis W, Jongejans E (2020) Declining abundance of beetles, moths and caddisflies in the Netherlands. Insect Conserv Divers 13:127-139</p> <p>Data were collected at two groups of sites: De Kaaistoep and Wijster, both in the Netherlands. In addition, we obtained data from two KNMI weather stations (for De Kaaistoep data: weather station Gilze-Rijen, for Wijster data: weather station Eelde, at, respectively, 3.6 and 40 km from trapping locations), from which we extracted relevant parameters.</p> <p><em>Collecting at light in De Kaaistoep</em></p> <p>De Kaaistoep is a 330 ha managed natural area consisting of heathland, pine forest and grassland. It was established in 1994 on former arable land. Information about the location and management history can be found in the study by Felix and van Wielink (2008). Insects were attracted by light in combination with a white cloth (Supporting Information Fig. S1) over a period of 3.3 h per trapping night, normally starting around sunset (Hallmann et al. 2020). During this sampling period, individuals of the various insect taxa were counted, or were estimated in the case of large numbers. All macro-moths were always counted and identified, while for other groups of insects, between 25 and 100% were collected for identification. Further details of the sampling protocol are given in the study by van Wielink and Spijkers (2013). The data archived her have been collected during 628 trapping nights between 1997 and 2017, on average 30 evenings per year (10–77). Data were available for the period of 1997–2017 for macro-moths (Lepidoptera), beetles (Coleoptera) and ground beetles (Carabidae), while for caddisflies (Trichoptera), lacewings (Neuroptera), true bugs (Hemiptera-Heteroptera and Hemiptera-Auchenorrhyncha) and mayflies (Ephemeroptera) data were available only for the years 2006 and 2009–2017. Of the large number of Coleoptera, only ground beetles, ladybirds and carrion beetles were identified to species up to 2017, accounting for 48 000 of 239 000 beetle specimens.</p> <p> As it is known that the environmental conditions (like temperature) during each trapping night influenced the number of insects caught, we aimed to include relevant covariates in our analyses. Information about the timing and duration of sampling were available for 91.2% of the nights (n = 574), and lacking more in the first few years of sampling than later on. The number of sampling hours per night varied little among years but did increase from an average of 3.1 h (1997–2009) to an average of 3.8 h per night after 2010 (Hallmann et al. 2020). Timing of onset of sampling was roughly at sunset throughout the years, with the exception of the first few years in which sampling started on average up to half an hour after sunset (Hallmann et al. 2020). </p> <p><em>Pitfall traps near Wijster</em></p> <p>A long-term monitoring program using pitfall traps was started at the Wijster Biological Station (and continued by the Foundation Willem Beijerink Biological Station) in two nature reserves in the province of Drenthe: National Park Dwingelderveld and the fragmented, but increasingly reconnected Hullenzand. In these reserves restoration measures, mainly in the form of topsoil removal and reconnection, were carried out during the early 1990s. The pitfall data have been collected between 1959 and 2016 at in total 48 unique locations (mean = 9, range 4–19 operating locations per year). The locations consisted mainly of heathlands, with some forest sites, a forest edge and an abandoned crop field. At each location, three square pitfall traps (25 by 25 cm) were installed: one lethal funnel trap with a 3% formaldehyde solution and two live traps. The traps at each location were spaced 10 m apart. Caught ground beetles (Coleoptera: Carabidae) have been identified at weekly intervals. Further details on the sampling protocol and the area are given in the study by den Boer and van Dijk (1994). Because we are only interested in recent trends in insect abundances, and because sampling protocols were not consistent in the early years, we here only archive data collected since 1986. We document two types of data: i) the annual sums per species and location for the period of 1986–2016, and ii) the weekly sums per species and location that have been fully digitised and checked: 2002–2017. </p> <p><em>Annual totals 1986–2016</em>. In total, 7778 records of species-specific counts are archived, which amounted to 264 986 individual ground beetles. For 20 records, we used multiple imputation (Onkelinx et al. 2017) to derive more reliable estimates for suspected erroneous counts. This method is based on the correlation structure between years and between other species. Note that in the years 1998–2001, no monitoring took place, and 2004 was omitted because of incomplete catches. </p> <p> </p> <p><em>Species weights</em></p> <p>For biomass estimation we used known species length measurements and known relationships of length to weight (Sabo et al. 2002; García-Barros 2015). For the Carabidae in the Wijster data set, we used the minimum and maximum body length as stated in the Dutch ground beetles field guide (Boeken et al. 2002). </p> <p> </p> <p>KaaistoepOrder.csv </p> <ul> <li>Trich = number of Trichoptera individuals</li> <li>Hemip = number of Hemiptera individuals</li> <li>Neur = number of Neuroptera individuals</li> <li>Ephem = number of Ephemeroptera individuals</li> <li>Hemi.hetero = number of Hemiptera-Heteroptera individuals</li> <li>Hemi.cica = number of Hemiptera- Auchenorrhyncha individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>start = date and start time of the measurement</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> </ul> <p> </p> <p>KaaistoepColeo.csv</p> <ul> <li>Coleo = number of Coleoptera individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>sunset = date and sunset time</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> </ul> <p> </p> <p>KaaistoepLepi.csv</p> <ul> <li>freq = number of Lepidoptera individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>sunset = date and sunset time</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> </ul> <p> </p> <p>KaaistoepCara.csv</p> <ul> <li>Cara = number of Carabidae individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>sunset = date and sunset time</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> <li><em>species</em> = number of individuals of each of 94 Carabidae species</li> </ul> <p> </p> <p>KaaistoepSilphi.csv</p> <ul> <li>Silph = number of Silphidae individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>sunset = date and sunset time</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> <li><em>species</em> = number of individuals of each of 6 Silphidae species</li> </ul> <p> </p> <p>KaaistoepCocci.csv</p> <ul> <li>Cocci = number of Coccinellidae individuals</li> <li>T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>U = hourly relative humidity (in %; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>FH = hourly average windspeed (in 0.1 m/s)</li> <li>sunset = date and sunset time</li> <li>suns.deviate = number of minutes the measurements started after sunset</li> <li>tdiff = number of hours of measurement</li> <li><em>species</em> = number of individuals of each of 23 Coccinellidae species</li> </ul> <p> </p> <p>KaaistoepMacro2.csv (non-zeros)</p> <ul> <li>freq = number of macro-Lepidoptera individuals</li> <li>name = macro-Lepidoptera species</li> <li>year = year of measurement</li> <li>daynr = day of the year</li> </ul> <p> </p> <p>KaaistoepLepiWeights</p> <ul> <li>name = macro-Lepidoptera species</li> <li>mgSabo = mass (mg) estimated using Sabo et al. (2002)</li> <li>mgGarciaBarros = mass (mg) estimated using García-Barros (2015)</li> </ul> <p> </p> <p>WijsterWeek.csv</p> <ul> <li>freq = number of ground beetle individuals</li> <li>date = date of emptying trap</li> <li>dt = number of days a trap has been open</li> <li>trap = randomized trap ID</li> <li>FG = hourly average windspeed (in 0.1 m/s)</li> <li>TG = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height)</li> <li>RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm)</li> <li>UG = hourly relative humidity (in %; at 1.5 meter height)</li> <li><em>species</em> = number of individuals of each of 134 ground beetle species</li> </ul> <p> </p> <p>WijsterColeoWeights</p> <ul> <li>name = ground beetle species</li> <li>mgSabo = mass (mg) estimated using Sabo et al. (2002)</li> </ul> <p> </p> <p>References</p> <p>Boeken M, Desender K, Drost B, van Gijzen T, Koese B, Muilwijk J, Turin H, Vermeulen R (2002) De Loopkevers van Nederland en Vlaanderen (Coleoptera, Carabidae). Jeugdbondsuitgeverij, Nederland</p> <p>den Boer PJ, van Dijk TS (1994) Carabid Beetles in a Changing Environment. 94‐6, Wageningen Agricultural University</p> <p>Felix R, van Wielink P (2008) On the biology of <em>Calodromius bifasciatus</em> and related species in De Kaaistoep (Coleoptera: Carabidae). Entomol Berichten 68:198–209</p> <p>García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterol, 38:59–74</p> <p>Hallmann CA, Zeegers T, van Klink R, Vermeulen R, van Wielink P, Spijkers H, van Deijk J, van Steenis W, Jongejans E (2020) Declining abundance of beetles, moths and caddisflies in the Netherlands. Insect Conserv Divers 13:127-139</p> <p>Onkelinx T, Devos K, Quataert P (2017) Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. J Ornithol 158:603–615</p> <p>Sabo JL, Bastow JL, Power ME (2002) Length–mass relationships for adult aquatic and terrestrial invertebrates in a California watershed. J North Am Benthol Soc 21:336–343</p> <p>van Wielink P, Spijkers H (2013) Insects nightly attracted to light at a single site in De Kaaistoep, The Netherlands. Orders, families and species identified in 1995–2011. Entomol Berichten, 73:200–214</p>
MER MB Relative Iron Abundance Data Bundle
This bundle contains relative iron abundances derived from from the Moessbauer Spectrometer (MB) on both Mars Exploration Rovers (Spirit and Opportunity).
Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing
Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.
Supplementary data for PhD Thesis - Appendix D.i: 16S community abundance and taxonomy data
<p>Abundance and taxonomic assignments from 3 Pyrosequencing runs, and 1 MiSeq run for chapters 2-5 of PhD Thesis.</p> <p>Data provided in tab- separated text format.</p>
Assessment of the adverse effects of pollution on farmland bird diversity in contemporary agricultural landscapes: Species Richness and Abundance Data
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GALILEO PROBE HELIUM ABUNDANCE DETECTOR DATA V1.0
This data set is most accurately described in [VONZAHN&HUNTEN1996]:
The false positive problem: An assessment of integrated abundance and occupancy models where false positives are present in ecological data.
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Data associated with: Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones.
<p>These are the soil organic matter fraction and environmental covariate data associated with the manuscript, " Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones." Please note that these data are currently for review only; our agreement with farmers currently limits the public availability of these data for any purposes other than review by assigned peer-reviewers. Following review, data will be shared following a reasonable request.</p>
Calculated abundances for krill, salp and species of zooplankton. Data collected aboard cruises off the coast of the Western Antarctic Penninsula, 1993 - 2004.
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SEGUE data for Bovy et al. (2012, 2013) mono-abundance populations papers
<p>This is the SEGUE data used in the Bovy et al. (2012, 2013) papers on the decomposition of the Milky Way disk in terms of mono-abundance stellar populations:</p> <ul> <li><a href="http://adsabs.harvard.edu/abs/2012ApJ...753..148B">The Spatial Structure of Mono-abundance Sub-populations of the Milky Way Disk</a>, Bovy, Rix, Liu, et al. (2012)</li> <li><a href="http://adsabs.harvard.edu/abs/2012ApJ...751..131B">The Milky Way Has No Distinct Thick Disk</a>, Bovy, Rix, & Hogg (2012)</li> <li><a href="http://adsabs.harvard.edu/abs/2012ApJ...755..115B">The Vertical Motions of Mono-abundance Sub-populations in the Milky Way Disk</a>, Bovy, Rix, Hogg, et al. (2012)</li> <li><a href="http://adsabs.harvard.edu/abs/2013ApJ...779..115B">A Direct Dynamical Measurement of the Milky Way's Disk Surface Density Profile, Disk Scale Length, and Dark Matter Profile at 4 kpc ~ R ~ 9 kpc</a>, Bovy & Rix (2013)</li> </ul> <p>This data can be used together with the code available <a href="https://github.com/jobovy/segue-maps">here</a> and <a href="https://github.com/jobovy/segueSelect">here</a> to reproduce the results in that paper. For completeness, similar data on K dwarfs in SEGUE is also included, but note that the selection function of these data is significantly more complicated than that of the G dwarf data.</p> <p>This dataset can almost entirely be reconstructed using the SQL queries given <a href="https://github.com/jobovy/segue-maps/tree/master/sql">here</a> on SDSS Sky Server, except for the [a/Fe] values from SDSS DR8, which were never made public. These [a/Fe] values are provided here <em>solely</em> for the purpose of reproducing the results from the above papers and should not be used for other purposes. Please contact the SEGUE team (Young-Sun Lee, Tim Beers, Connie Rockosi) if you want access to these data for other purposes.</p>
GALILEO PROBE HELIUM ABUNDANCE DETECTOR DATA V1.0
This data set is most accurately described in [VONZAHN&HUNTEN1996]:
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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.