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Mollusc population size distribution monitoring: Fall 2019 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2019 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-2007. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning
Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between
Species Distribution Modeling of Carnivorous Plants Worldwide
Forecasting how carnivorous plant species will respond to climatic change is a key issue in their conservation and management but presents a number of challenges. These challenges derive from interactions between the relatively simplistic statistical methods typically used to forecast species responses to climatic change, which to date have been limited mainly to species distribution models (“SDMs) and particular aspects of the ecology of carnivorous plants, including their rarity, habitat specialization, and limited dispersal ability. The small ranges and oftentimes low local abundance of carnivorous plants provide few occurrence records, which increase the potential for poorly or over-fitted SDMs and misspecification of relationships with their “optimal” environments. The unique habitats in which carnivorous plants often grow also are difficult to characterize using the basic temperature and precipitation data that often undergird SDMs. Rather, habitats in which carnivorous plants are common often are decoupled from broader climatic patterns (e.g., many retain high soil moisture even during seasonal drought) and may be associated with frequent disturbance. Last, dispersal limitation also may constrain range shifts of carnivorous plants as the climate changes. These three issues raise two related questions that are critical for understanding and forecasting the future of carnivorous plants. First, to what extent are current carnivorous plants distributions constrained by climate; and second, how readily, if at all, might carnivorous plants disperse to colonize new habitat as it becomes climatically suitable? We estimated the vulnerability of carnivorous plants to climatic change in light of challenges identified with SDMs in general and their particular application to these unique species. We combined two approaches: “ensembles of small models”, which attempt to deal with the challenges of fitting SDMs for data-limited species; and “bioclimatic velocity”, which is
Soil chemistry and nutrient distribution in long-term NPP plots at the Jornada Basin LTER site, 1989
This dataset contains soil nutrient and chemistry data from soils collected in 1989 at the 15 long-term NPP study sites at the Jornada Basin LTER site. The study sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. In 1989, replicate soil samples were collected at multiple depths from buffer zones surrounding the long-term vegetation monitoring plots. Nutrient and chemical analyses were subsequently performed on these soils, and the data here include inorganic nitrogen, phosphorus, pH, cation concentration, percent organic carbon, and other variables. This dataset is complete.
Soil chemistry and nutrient distribution in long-term NPP plots at the Jornada Basin LTER site, 1991
This dataset contains soil nutrient and chemistry data from soils collected in 1991 at the 15 long-term NPP study sites at the Jornada Basin LTER site. The study sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. In fall of 1991, replicate soil samples were collected at multiple spatial grid designs in the biomass plots of the long-term vegetation monitoring plots. Nutrient and chemical analyses were subsequently performed on these soils, and the data here include total nitrogen, pH, gravimetric water content, and other variables. This dataset is complete.
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009
Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Urchin size frequency distribution
These data describe the size frequency distribution of red (Mesocentrotus franciscanus) and purple (Strongylocentrotus purpuratus) sea urchins within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The diameter of the test (shell without spines) was recorded to the nearest 0.5 cm for 50 red and 50 purple sea urchins located within a 40 m x 2 m area of each plot. Size frequency data of red and purple sea urchins are not collected in the continual kelp removal plots. When combined with size-mass relationships established in the laboratory these data were used to provide a non-destructive, in situ estimate of the dry mass per unit area of bottom for each species. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.
SBC LTER: Reef: Long-term experiment: Kelp removal: Urchin size frequency distribution
These data describe the size frequency distribution of red (Mesocentrotus franciscanus) and purple (Strongylocentrotus purpuratus) sea urchins within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The diameter of the test (shell without spines) was recorded to the nearest 0.5 cm for 50 red and 50 purple sea urchins located within a 40 m x 2 m area of each plot. Size frequency data of red and purple sea urchins are not collected in the continual kelp removal plots. When combined with size-mass relationships established in the laboratory these data were used to provide a non-destructive, in situ estimate of the dry mass per unit area of bottom for each species. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
The DNNLikelihood: enhancing likelihood distribution with Deep Learning
<p>Datasets and trained models corresponding to version 2 of <a href="https://arxiv.org/abs/1911.03305">arXiv:1911.03305</a> and complementing the code on <a href="https://github.com/riccardotorre/DNNLikelihood/releases/tag/1911.03305v2">GitHhub</a>.</p> <p>Notice that the code on GitHub includes scripts to automatically download these data.</p> <p> </p>
Great Britain's hourly natural gas demand at a local level (distribution level) from 2017-01 to 2018-03
<p>An hourly local natural gas demand dataset created for the UK Energy Research Centre's Phase 3 FlexiNET project.</p> <p>A briefing note using part of the data can be found - http://www.ukerc.ac.uk/publications/local-gas-demand-vs-electricity-supply.html</p> <p>The dataset has been aggregated from the hourly operational demand data from Great Britain's four Gas Distribution Network companies (GDNs) and provides an empirical record rather than modelled data.</p> <p>Columns ['utc_index', 'utc_aware', 'utcdiff', 'localtime_aware', 'localtime_naive_text', 'localtimediff', 'gb_demand_kWh_cleaned']</p> <p>Column descriptions:</p> <p>utc_index: datetime in utc</p> <p>utc_aware: datetime in utc timezone aware</p> <p>utcdiff: difference between subsequent values for utc_aware column (as check step value - should only be 0 days 01:00:00.000000000)</p> <p>localtime_aware: London local time</p> <p>localtime_naive_text: London local time as text</p> <p>localtimediff: difference between subsequent values for localtime_aware column (as check step value - should have one value per year of 02:00 hours for clock forward in March to British Summer Time, 00:00 for clock backward in October, and 01:00 for all other values)</p> <p>gb_demand_kWh_cleaned: the aggregate demand for natural gas through the local gas networks in kWh over the hour</p>
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Data for "Soil CO2 efflux errors are lognormally distributed - Implications and guidance."
<p>Soil CO2 flux data at site ES-LMa of four automatic chambers in the control-openLand-subplot for the period from 2015-11-10 to 2016-11-10.</p> <p>These data were used for the publication:</p> <p>Wutzler, et al. (2020) "Soil CO2 efflux errors are lognormally distributed - Implications and guidance." Geoscientific Instrumentation, Methods, and Data Systems</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p> <p> </p>
Revealing Hidden Orbital Pseudospin Texture with Time-Reversal Dichroism in Photoelectron Angular Distributions
<p>Angle-resolved photoemission spectroscopy (ARPES) of bulk 2H-WSe2 for different crystal orientations linked to each other by time-reversal symmetry. This dataset supplements a manuscript, and was used to measure a new observable called time-reversal dichroism in photoelectron angular distributions (TRDAD), which quantifies the modulation of the photoemission intensity upon effective time-reversal operation. Experimental results are in quantitative agreement with both tight-binding model and state-of-the-art fully relativistic calculations performed using the one-step model of photoemission, unambiguously demonstrating that TRDAD reveals its orbital pseudospin texture counterpart.</p>
Habitatquarries: distribution of underground marl quarries in the Flemish Region and border areas, with the Flemish distribution of Natura 2000 habitat type 8310
<p><strong>General</strong></p> <p>The data source is a geospatial collection of polygons that correspond with the presence or absence of the Natura 2000 Annex I habitat type 8310 (Caves not open to the public) in the Flemish Region (and border areas), Belgium. </p> <p>The dataset contains all known, not collapsed, underground marl quarries in Flanders. Several of these quarries have their entrance in or run underground to the neighboring regions/countries.</p> <p>In general, different polygons represent different quarry units with their own internal climatic environment. Units that cross Flemish borders have been split into separate polygons. Exceptionally they may overlap if such units are situated above each other. </p> <p>For safety reasons, the dataset only contains the contour of the quarries, and no details like floor plans or entrances. For admission to research the indoor climate, please contact the Quarries and Safety Department of the municipality of Riemst (<a href="https://www.riemst.be/nl/wonen/groeven">https://www.riemst.be/nl/wonen/groeven</a>; <a href="mailto:mike.lahaye@riemst.be">mike.lahaye@riemst.be</a>).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, a scientific institute of the Flemish government).</p> <p> </p> <p><strong>Technical aspects</strong></p> <p>The data source is a GeoPackage that contains:</p> <ul> <li> <p>a spatial polygon layer ‘<code>habitatquarries</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p>a non-spatial table ‘<code>extra_references</code>’ with site-specific bibliographic references.</p> </li> </ul> <p>The data source has been based on an unpublished shapefile used in De Saeger & Lahaye (2019) and on a BibTeX bibliography file. See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/c0821eb/src/generate_habitatquarries">'n2khab-preprocessing' at commit c0821eb</a> for the creation.</p> <p>A reading function to return <code>habitatquarries</code> (this data source) in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The attributes of the spatial polygon layer ‘<code>habitatquarries</code>’ are: </p> <ul> <li> <p><code>polygon_id</code>: a unique number per polygon; </p> </li> <li> <p><code>unit_id</code>: a unique number for each quarry unit. Quarry units consisting of several polygons (= partly outside the Flemish region) have a number greater than 100;</p> </li> <li> <p><code>name</code>: name of the site;</p> </li> <li> <p><code>habitattype</code>: either:</p> <ul> <li> <p><code>8310</code> (habitat type 8310)</p> </li> <li> <p><code>gh</code> (no Natura 2000 type)</p> </li> <li> <p>missing (outside of the Flemish Region);</p> </li> </ul> </li> <li> <p><code>extra_reference</code>: extra reference with more information.</p> </li> </ul> <p>The non-spatial table <code>extra_references</code> provides the bibliography referred to by the spatial attribute <code>extra_reference</code>. It was derived from a BibTeX bibliography file by using the R-package <a href="https://docs.ropensci.org/bib2df">bib2df</a>, and it is back-convertible into one (see R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>). The original bibliography file is also available in the above linked ‘n2khab-preprocessing’ repository.</p>
Data from: Thermal niches of wheat curl mite, Aceria tosichella (Acari: Eriophyidae): congruence between physiological and geographical distribution data
<p><strong>Filename: parm.csv</strong></p> <p>Population growth rate of the two wheat curl mite (WCM) lineages reared in different temperatures.</p> <ol> <li>lineage - mitochondrial lineage (MT-1 or MT-8)</li> <li>temp - the rearing temperature (ºC)</li> <li>n - no. of replications of the experiment</li> <li>r, r.lower, r.upper - estimated intrinsic population growth rate and its 95% confidence intervals</li> </ol> <p><strong>Filename: aceria.csv</strong></p> <p>Data on field sampling localities, WCM abundance and thermal niche suitabiity.</p> <ol> <li>julian - Julian date</li> <li>x, y - geodetic coordinates (EPSG: 2180)</li> <li>stems - no. of stems collected</li> <li>MT1, MT8- mitochondrial lineage (MT-1 or MT-8)</li> <li>TNS1, TNS8 - thermal niche suitability for lineages</li> </ol>
Modelled distributions of fish and epibenthic invertebrates in the southern North Sea
<p>These data include distribution maps of fish and invertabrate species in the southern North Sea from 2014 until 2023. The maps are modelled using point data of presence/absence and biomass (per trawled km²) from scientific fisheries surveys to estimate the distribution of the probability of occurrence (POC) or biomass (kg per km²), respectively. Also included are forecasts of species' distributions assuming increasing water temperatures in the southern North Sea according to the ICCP scenario RCP8.5.</p> <p>Each files contains a raster stack with layers for each species. The data can be read into the R using the 'stack'-command from the 'raster'-package. The raster stacks contain layers with headers, which code the species and size group. For some species of relevance to fisheries managment, Numbers behind the latin names of the species give information on the included size classes in cm with 'no' indicating no size class information was available.</p> <p>The file names are composed of the follwing elements:</p> <p>'bio' = biomass</p> <p>'poc' = probability of occurrence</p> <p>'emp' = observed occurrence/abundance data from fisheries surveys with employed spatial smoother</p> <p>'sdm' = modelled distributin data from random forests</p> <p>'fc' = forecast distributions based on temperature predictors according to RCP8.5</p> <p>'rel.ca2' = core areas (CA) of distribution representing values > then the mid-point of modelled POC value range</p> <p>Year numbers give the time frame of empirical data or model predictions. </p> <p> </p> <p><strong>You can access the .tiff-files with the following R-commands using the directory path where you have stored the files:</strong></p> <p><em><strong>library(raster)</strong></em></p> <p><em><strong>poc<-stack("your_path/poc.sdm.2014_2023.tiff")</strong></em></p> <p><em><strong>poc$gadus.morhua_5_113 </strong># Plots distribution of Atlantic cod as probability of occurrence observed at a size range from 5 - 113 cm tail length</em></p>
Distribution-wide morphometric data of Jungle Crows (Corvus macrorhynchos)
<p>Here we present a dataset derived from standardised photography of museum specimens of Jungle Crows (<em>Corvus macrorhynchos</em>), a widespread Asian Corvid. We photographed 1105 crows, of which 1069 we managed to collect measurements of hard tissue (i.e., bill characteristics and tarsus length). We combined these measurements with museum curated data on the locality of the specimens, resulting in a geo-tagged dataset of crow morphology. The measured crows originated from across their distribution, representing the most comprehensive morphometric dataset for <em>Corvus macrorhynchos</em> to date.</p> <p>The data consists of three .csv files, and five zip files:</p> <ul> <li>Museum Crow Measurements Metadata.csv contains the information on the columns contained in Museum Crow Measurements.csv split into Column and Details, where column names match those found in Museum Crow Measurements.csv, and details provide information on the data within that column.</li> <li>Museum Crow Measurements.csv represents the core data table containing all hard tissue measurements of C. macrorhynchos alongside museum derived field and manually review location fields. Each row equals an individual specimen and missing data are denoted with <NA>.</li> <li>Museum Crow Measurements Epicollect.csv contains the information collected alongside the images taken via Epicollect. The data was used to link photos and subsequent measurements to museum metadata.</li> </ul> <p>Within the zip files are JPEG versions of the crow specimen photographs, seperated into dated folders, named with the original CANON folder they were saved in, the original image number they were created with, and the crow ID (C###{Canon folder number}_IMG_####{Image Number}_####{crow ID}.JPEG). The image number and crow ID match with information contained in Museum Crow Measurements Epicollect.csv. The information contained in Museum Crow Measurements Epicollect.csv pertaining to the number of first image was the basis for renaming the image files.</p> <ul> <li>ANMH_{YYYY-MM-DD}.zip. The American Museum of Natural History in New York, USA</li> <li>CUMV_{YYYY-MM-DD}.zip. The Cornell Lab of Ornithology in Ithaca, USA</li> <li>FMNH_{YYYY-MM-DD}.zip. The Field Museum of Natural History in Chicago, USA</li> <li>NHMUK_{YYYY-MM-DD}.zip. The Natural History Museum at Tring, UK. Images for 2024-03-13 are split into two parts, indicated by _#, to reduce individual file size.</li> <li>USNM_{YYYY-MM-DD}.zip. The Smithsonian National Museum of Natural History in Washington, D.C., USA</li> </ul> <p>Full description of the data can be found at <a href="https://doi.org/10.1016/j.dib.2025.111325" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2025.111325</a> (Alamshah, A. L., & Marshall, B. M. (2025). Distribution-wide morphometric data of Jungle Crows (Corvus macrorhynchos). <em>Data in Brief</em>, 111325.)</p> <p>Citations for use of this data are below:</p> <p>@article{alamshah_distribution-wide_2025,<br> title = {Distribution-wide morphometric data of {Jungle} {Crows} ({Corvus} macrorhynchos)},<br> issn = {23523409},<br> url = {https://linkinghub.elsevier.com/retrieve/pii/S2352340925000575},<br> doi = {10.1016/j.dib.2025.111325},<br> language = {en},<br> urldate = {2025-01-24},<br> journal = {Data in Brief},<br> author = {Alamshah, Aubrey Lynn and Marshall, Benjamin Michael},<br> month = jan,<br> year = {2025},<br> pages = {111325},<br>}</p> <p>@misc{alamshah_distribution-wide_2024,<br> title = {Distribution-wide morphometric data of {Jungle} {Crows} ({Corvus} macrorhynchos)},<br> copyright = {Creative Commons Attribution 4.0 International},<br> url = {https://zenodo.org/doi/10.5281/zenodo.12788353},<br> doi = {10.5281/ZENODO.12788353},<br> language = {en},<br> urldate = {2025-05-08},<br> publisher = {Zenodo},<br> author = {Alamshah, Aubrey and Marshall, Benjamin Michael},<br> month = jul,<br> year = {2024},<br> keywords = {Zoology, FOS: Biological sciences, Evolutionary biology, Ornithology},<br>}</p> <p>This data has been used in the following manuscript:</p> <p>@article{alamshah_big_2025,<br> title={Big bills, small changes: with few exceptions, Jungle crows show minor variation in bill morphology across their distribution},<br> author={Alamshah, Aubrey Lynn and Marshall, Benjamin Michael},<br> year={2025},<br> publisher={EcoEvoRxiv},<br> doi={https://doi.org/10.32942/X2NW74}<br>}</p>
Size distribution of neutral and charged particles smaller than 42 nm measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>The size distribution of neutral and charged particles was measured using a neutral cluster and air ion spectrometer (NAIS) instrument. The concentration was corrected for diffusional losses in the inlet.</p> <p>The concentration and temporal dynamics of small particles is fundamental to characterize the first step of new particle formation (NPF) and growth. Moreover, naturally charged particles and ions can provide information about the role of ion induced nucleation. Newly formed particles can grow to larger sizes where they act as cloud condensation nuclei, directly affecting the Earth radiative budget and cloud properties.</p> <p>Measurements were performed on the upper deck of icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to April 11, 2017. The concentration is reported as dN/dlog(Dp) per cubic centimetre, where Dp indicates the corresponding diameter size bin. Data were collected with one-second time resolution and averaged automatically by the acquisition software to 120 seconds before January 31 2017 and to 90 seconds after that date. The instrument was calibrated before the campaign by the manufacturer and periodically cleaned during the campaign (one time per leg).</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al., 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 0 polluted data).</p> <p> </p> <p>***** Dataset contents *****</p> <p>- 01_neutral_particles_size_distribution.csv, data file, comma-separated values</p> <p>- 02_negative_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 03_positive_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 04_neutral_particles_size_distribution_header.txt, metadata, text</p> <p>- 05_negative_ions_size_distribution_header.txt, metadata, text</p> <p>- 06_positive_ions_size_distribution_header.txt, metadata, text</p> <p>- README.txt, metadata, text</p> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</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.