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18,621 results for “development;”
Data from: Torchtree: flexible phylogenetic model development and inference using PyTorch
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Accelerating development in UAV network digital twins with a flexible simulation framework
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Sex‐dimorphic effects of biogenesis of lysosome‐related organelles complex‐1 deficiency on mouse perinatal brain development
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Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids
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Data from: Laboratory culture of the California Sea Firefly Vargula tsujii (Ostracoda: Cypridinidae): developing a model system for the evolution of marine bioluminescence
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Data from: Modifications during early plant development promote the evolution of nature’s most complex woods
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Developing an efficient dispatching strategy to support commercial fleet electrification
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Data from: Consecutive treatments of methamphetamine promote the development of cardiac pathological symptoms in zebrafish
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Development of a tool for quantifying need-supportive coaching in technology-mediated exercise classes
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SVACH - Development of advanced smart ventilation controls for residential applications
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Spatial-temporal change of climate in relation to urban fringe development in central Arizona-Phoenix
Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear re
Developing seedheads treated with fungicide and insecticide:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
iSCAPE Low Cost Sensor Development Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the tests used for the low-cost sensor development during the iSCAPE project. The dataset is divided in a series of tests, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. The conditions of the test and the purpose vary, and their reports are also included.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air Temperature (ºC) Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (% REL) Sensirion SHT-31 [HUM]</li> <li>Noise Level (dBA) Invensense ICS-434342 [NOISE_A]</li> <li>Ambient Light (Lux) Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]</li> <li>Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A]</li> <li>Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A] </li> <li>Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A]</li> <li>Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]</li> <li>Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]</li> <li>PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station</li> <li>PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station</li> </ul> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers:</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_054</li> <li>DS_TS_062</li> <li>DS_TS_063</li> <li>DS_TS_065</li> <li>DS_TS_067</li> <li>DS_TS_068</li> <li>DS_TS_069</li> <li>DS_TS_070</li> <li>DS_TS_071</li> <li>DS_TS_072</li> <li>DS_TS_073</li> <li>DS_TS_074</li> <li>DS_TS_075</li> <li>DS_TS_076</li> <li>DS_TS_077</li> <li>DS_TS_078</li> <li>DS_TS_079</li> <li>DS_TS_080</li> <li>DS_TS_081</li> <li>DS_TS_084</li> <li>DS_TS_088</li> <li>DS_TS_089</li> <li>DS_TS_090</li> <li>DS_TS_092</li> </ul> <p> </p>
Icons for developed analytical methods.
<p>Logo images for bioinformatic implementations</p>
The Development of Digelius Electronics Finland (DEF) in the Documentary Evidence
<p>The spreadsheet consists of notes about the appearances of Digelius Electronics Finland in the historical documentary evidence depicting the rise and fall of the company. Digelius Electronics Finland was a Finnish small scale industry engineering company initially founded to design and build Erkki Kurenniemi's electronic musical instruments. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p>
FIGURE 16 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 16. Hermannia scabra (L. Koch, 1879), tritonymph: A—leg I, right, antiaxial view; B—leg II, right, antiaxial view; C—leg III, left, antiaxial view; D—leg IV, left, antiaxial view. Scale bars 50 μm.
FIGURE 20 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 20. Hermannia scabra (L. Koch, 1879), tritonymph, SEM micrographs: A—part of gastronotic surface with folds and notogastral seta d1, dorsal view; B—part of gastronotic region with folds, tuberculate swellings of folds and gastronotic seta c3, lateral view. Scale bars 10 μm (A), 20 μm (B).
FIGURE 13 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 13. Hermannia scabra (L. Koch, 1879), nymphs: A, B, C—epimeral region of proto-, deuto- and tritonymphs, respectively. Scale bar 50 μm.
FIGURE 10 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 10. Hermannia scabra (L. Koch, 1879), larva: A—dorsal view (legs omitted); B—ventral view (gnathosoma and legs except trochanters omitted). Scale bar 100 μm.
FIGURE 8 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 8. Hermannia scabra (L. Koch, 1879), adult, microscope images: A—lateral view; B—ventral view; C—lateral view.
ScienceDex guides
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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.