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28,952 results for “Distribution”
Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland
<p>Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".</p> <p>Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.</p> <p>The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.</p>
DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring
<p> </p> <p> </p> <p>This dataset aims to support the work presented in</p> <blockquote> <p>Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>.</p> </blockquote> <p>It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices. </p> <p>The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m).</p> <p>The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format <em>YYYYMMDD</em>, UTC time at the beginning of the file, channels, whale_raw, duration of the file L<em>xx</em>s, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example </p> <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a> <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <p><strong>Each file contains the following variables</strong></p> <ul> <li><em>data: </em>The DAS-recorded nano strain data</li> <li><em>info_GL_m:</em> Used gauge length (m)</li> <li><em>info_nsamples</em>: Number of temporal samples in the file</li> <li><em>info_ntraces</em>: Number of spatial samples (channels) in the file</li> <li><em>info_sample_interval_s</em>: Sampling period (s)</li> <li><em>info_sampling_frequency_Hz</em>: Sampling frequency (Hz)</li> <li><em>info_SSI_m</em>: Spatial sampling interval (m)</li> <li><em>info_timestamp</em>: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li><em>x1_absolute_channel</em>: Vector containing the absolute channel number</li> <li><em>x1_distance_from_shore_m</em>: Vector containing the distance along the FO cable from shore (m)</li> <li><em>x1_position_m</em>: Vector containing the distance along the FO cable from the interrogator (m)</li> <li><em>x1_recwdepthz_m</em>: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li><em>x1_relative_channel</em>: Vector containing the channel number</li> <li><em>x2_time_s</em>: Time vector (s)</li> </ul> <p> </p> <p><strong>List of the files and related manuscript examples</strong></p> <p>Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC</p> <ul> <li><em>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat</em> </li> </ul> <p>Example of<strong> </strong>series of blue whale calls recorded with a move out on the Svalbard DAS array - Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC</p> <ul> <li><em>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</em></li> </ul> <p>Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC</p> <ul> <li><em>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat </em></li> <li><em>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </em></li> </ul> <p><strong>--------------</strong></p> <p><strong>Analysis tools </strong></p> <p>To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>:</p> <blockquote> <p>Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a></p> </blockquote> <p>Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a></p> <p><strong>--------------</strong></p> <p><strong>Please cite as </strong></p> <blockquote> <p>Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>.</p> </blockquote> <p><strong>--------------</strong></p> <p><strong>Contact</strong></p> <p><a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a></p>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Distribution of waterbirds along the Drugeon river, France
<p>This dataset describes bird observations performed along the Drugeon river, France from 2006 to 2019.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Carte.jpg?download=1"><strong>Carte.jpg</strong></a>' Map of the study area. The box shows the location of the Bannans and the Sainte-Colombe transects. Village locations and the route of the river have been taken from 'BD Carto ', kindly provided for research by the National Geographical Institute, and modified on the basis of field observations.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%201%20transects%20Drugeon%20synthe%CC%80se_Zenodo.xlsx?download=1"><strong>Mat sup 1 transects Drugeon synthèse_Zenodo.xlsx</strong></a>' since September 2006, two transects were carried out four times a fortnight,on foot in the early morning. All identified birds, by sight or by ear, were noted with special care to avoid double counting. The two transects are located in the downstream part of the Drugeon valley. The Bannans transect starts from the Drugeon diversion upstream from the Bannans flour mill and runs along the river downstream to the place called Mitray, with a lateral extension to the En Vau-Les-Aigues marsh located partly in the La Rivière-Drugeon village. This transect is approximately 5.4 km long, of which 1.4 km follow the river. It sampled the bird population of the river, the marshes more or less wooded with willows, birches and a few spruces, and the neighboring meadows and pastures. The flour mill dam provides a 3660 square meter body of water that is not flushed out because it is attached to a dwelling house. This is the only non-huntable wetland area along the two transects. The Sainte-Colombe transect starts from the village, crosses the mesophilic and then wet meadows to reach almost the downstream end of the Bannans transect. It then runs along the river in its undisturbed part to the Chaffois mill. The return to the village is via an agricultural path that crosses mesophilic and humid meadows, a marsh and a small wood of poplar and spruce trees. This transect is 8.6 km long including 2 km along the river. A U-shaped pond carved out of a marsh provides an open water surface of approximately 3400 square meters. It was also prospected during the transect.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%202%20donne%CC%81es%20brutes%20descente%20du%20Drugeon.xlsx?download=1"><strong>Mat sup 2 données brutes descente du Drugeon.xlsx</strong></a>' covers the censuses along the course of the Drugeon river from 2014 to 2019. Birds were recorded during a course carried out on foot along the river on a bank from the Dompierre bridge located between Vaux-et-Chantegrue and Bonnevaux villages to the Pont Rouge bridge next to Vuillecin village (29,2 km representing almost the entire course of the Drugeon river). Each year, the census was taken in sections during the second half of October, an average of one to one and a half months after the opening of the hunting season. Each waterbird observed was precisely located on a map and then plotted on Google Earth. Using Géoportail and field surveys, a precise description of the watercourse has been carried out on the whole of the prospected area: environment bordering each bank (forest, wooded marsh, herbaceous marsh, meadow, village), vegetation on each bank (continuous willow, megaphorbiaie with scattered willows, pure megaphorbiaie, phragmitaie, short herbaceous vegetation), width of the river, slope of the watercourse, status with respect to hunting (huntable zone, zone not huntable because located less than 150 m from homes and hunting reserve).</p> <p><em>The figures of the following three files are computed from the two files above: Mat sup 1 and Mat sup 2:</em></p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 3 Down river walk.zip?download=1">Mat sup 3 Down river walk.zip</a>'</strong> Distribution and abundance 2014-2019 of the main waterbird species along the Drugeon river</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 4 Hunting season onset.zip?download=1">Mat sup 4 Hunting season onset.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 5 Bannans transect.zip?download=1">Mat sup 5 Bannans transect.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/transectsDomi.kml?download=1">TransectsDomi.kml</a>' </strong>a kml file locating the Bannans and Sainte-Colombe transects (polylines).</p>
Data from calculated radial neutron flux distributions in a KBS-3 type geological repository
<p>Data from calculations of radial distribution of neutron flux per emitted neutron from rods of spent nuclear fuel in a KBS-3 type geological repository. Reference (<em>Jansson, 2022</em>) contain a summary of the calculations and a description of the structure of this data.</p> <p>This data was computed on resources provided by Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), National Supercomputer Centre at Linköping University (NSC) and the SNIC Cloud, partially funded by the Swedish Research Council through grant agreement no. 2018-05973, under projects SNIC 2021/5-299 and SNIC 2021/18-12.</p>
Data on 'Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021'
<p>This dataset includes raw and analysed data from '<strong>Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021</strong>'</p> <p><strong>Louise G. Køhler<sup>1</sup>, Bastian Huwer<sup>2</sup>, José Martín Pujolar<sup>1</sup>, Malin Werner<sup>3</sup>, Karolina Wikström<sup>3</sup>, Anders Wernbo<sup>3</sup>, Maria Ovegård<sup>3</sup>, Cornelia Jaspers<sup>1*</sup></strong></p> <p> </p> <p><sup>1</sup>Centre for Gelatinous Zooplankton Ecology and Evolution, National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 202, 2800 Kgs. Lyngby, Denmark</p> <p><sup>2</sup>National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 201, 2800 Kgs. Lyngby, Denmark</p> <p><sup>3</sup>Institute of Marine Research, Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences, Turistgatan 5, S- 453 30 Lysekil, Sweden</p> <p>* Corresponding author: <a href="mailto:coja@aqua.dtu.dk">coja@aqua.dtu.dk</a></p> <p>This dataset includes data on the qualitative and quantitative description of the gelatinous macrozooplankton community of the North Sea during January-February 2021. Sampling was conducted during the 1<sup>st</sup> quarter International Bottom Trawl Survey (IBTS) on board the Danish R/V DANA (DTU Aqua Denmark) and the Swedish R/V Svea (SLU Sweden), as part of the ichthyoplankton investigation during night-time. A total of 147 stations were investigated in the western, central and eastern North Sea as well as the Skagerrak and Kattegat. Sampling was conducted with a 13 m long Midwater Ring Net (MIK net, Ø 2 m, mesh size 1.6 mm, cod end with smaller mesh size of 500 µm), equipped with a flow meter. The MIK net was deployed in double oblique hauls from the surface to c. 5 m above the sea floor. Samples were visually analysed unpreserved on a light table and/or with a stereomicroscope or magnifying lamp within 2 hours after catch. A total of 13,610 individuals were counted/sized. Twelve gelatinous macrozooplankton species or genera were encountered, namely the hydrozoan <em>Aequorea vitrina</em>, <em>Aglantha digitale</em>, <em>Clytia</em> spp., <em>Leuckartiara octona,</em> <em>Tima bairdii, Muggiaea atlantica</em>; the scyphozoans <em>Cyanea</em> <em>capillata and Cyanea lamarckii</em> and the ctenophores <em>Beroe</em> spp., <em>Bolinopsis infundibulum</em>, <em>Mnemiopsis leidyi</em>, <em>Pleurobrachia pileus</em>. Abundance data are presented on a volume specific (m<sup>-3</sup>) and area specific (m<sup>-2</sup>) basis. Size data have been used to estimate wet weights based on published length-weight regressions (see reference column in the dataset). This dataset contributes baseline information about the gelatinous macrozooplankton diversity and its specific distribution pattern in the extended North Sea area during winter (January-February) 2021. These data can be an important contribution to address global change impacts on marine systems, especially considering gelatinous macrozooplankton abundance changes in relation to anthropogenic stressors.</p>
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</p>
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
GLOBAL SNAPSHOT Physician Distribution and Density of Physicians per 1000 population - Worldwide 2021
<p>The chart presents the most up-to-date data (2021) available for 49 of the world’s 195 countries, focusing on the total number of physicians and the number of physicians per 1000 population(1). The countries are categorized into four income groups based on World Bank classifications, which are updated annually on July 1st each year(2).</p> <p>Only 25% of the countries present current data. This information is critical for decision-making for healthcare planning and policy development. Equally crucial, is for researchers to have comparable data to propose initiatives, to establish benchmarks and for crafting holistic strategies to gauge and advance progress in healthcare systems globally.</p> <p>Data sources: UnData <a href="https://data.un.org/">https://data.un.org/</a></p> <p>Visualization tools used: RAWGraphs <a href="https://www.rawgraphs.io/">https://www.rawgraphs.io/</a>, MS PowerPoint and Microsoft Excel</p> <p>Intended Audience: Academics and Researchers; Students and Educators; Healthcare Administrators and Policy Makers; Non-Governmental Organizations</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751</p> <p> </p> <p>References:</p> <p>1. United Nations, Department of Economic and Social Affairs. 10 Health Personnel. In: Statistical Yearbook. 66th issue (2023). New York: United Nations; 2023. (ST/ESA/STAT/SER.S/42). [Dataset available at UnData] <a href="https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv">https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv</a></p> <p>2 World Bank. World Bank Country and Lending Groups. World Bank Data Help Desk [Internet]. [cited 2024 Apr 5]. Available from:<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"> https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a></p>
Distribution maps of vegetation alliances in Europe
<p>This dataset contains information on the occurrence of phytosociological alliances in European territorial units. The first version, including a description of the methods, was published by Preislerová et al. (2022).</p> <p>Version 2 of the dataset contains data on 1115 alliances (as opposed to 1105 in the first version) in 82 European territorial units. These changes reflect the concepts accepted in version 3 of the EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>), which were adopted by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data include a spreadsheet with the database and a set of 1115 maps as individual image files.</p> <p><strong>Recommended citation of the dataset</strong></p> <p>Preislerová Z., Jiménez-Alfaro B., Mucina L., Berg C., Bonari G., Kuzemko A., Landucci F., Marcenò C., Monteiro-Henriques T., Novák P., Vynokurov D., Bergmeier E., Dengler J., Apostolova I., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Daniëls F.J.A., De Sanctis M., Didukh Ya., Dítě D., Fanelli F., Golovanov Y., Golub V., Guarino R., Hájek M., Iakushenko D., Indreica A., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kucherov I., Küzmič F., Lebedeva M., Loidi J., Lososová Z., Lysenko T., Milanović Đ., Onyshchenko V., Perrin G., Peterka T., Rašomavičius V., Rodríguez-Rojo M.P., Rodwell J.S., Rūsiņa S., Sánchez Mata D., Schaminée J.H.J., Semenishchenkov Y., Shevchenko N., Šibík J., Škvorc Ž., Smagin V., Stešević D., Stupar V., Šumberová K., Theurillat J.-P., Tikhonova E., Tzonev R., Valachovič M., Vassilev K., Willner W., Yamalov S., Večeřa M. & Chytrý M. (2022). Distribution maps of vegetation alliances in Europe. <em>Applied Vegetation Science</em>, 25, e12642. https://doi.org/10.1111/avsc.12642</p>
SoA of measuring devices installed in NG transmission and distribution networks
<p>Deliverable D1.1 aims to design the state of the art of measuring devices in natural gas transmission and distribution networks. </p> <p>Transporting green hydrogen into existing gas assets requires carefully assessing its effect on the existing components. Since several projects have already been completed or have planned research activities to answer still-existing technical questions, the THOTH2 project focuses on the existing measuring devices. Specifically, the focus of the project regards the identification of the existing gaps in normative standards and the suggestions for solutions to cover them (if any). To contribute the hydrogen readiness of the existing gas transport and distribution infrastructures, new methodologies and protocols have to be developed to perform validated tests for metering devices. Suggestions on the need to change the standards or develop new ones will be based on the results of these experimental tests. Despite the simplicity of the methodological approach, it would be very critical when applying it to measuring devices. Several technologies are available in the market to measure gas properties. Furthermore, the operators can select more than one configuration based on the expected field conditions.</p> <p>Since limited resources are available, testing all the possible configurations would be impossible. Prioritization is required. Task 1.1 aims to collect all the information to provide a clear overview of the measuring devices installed in the existing gas assets. Specifically, this document includes the state of the art of measuring devices installed in gas assets. Different technologies are available to measure gas parameters. For example, turbine, rotary piston, ultrasonic, diaphragm, thermal mass, orifice, and Coriolis meters are available to measure flow rate. These technologies differ not only for the operating principle but also for the material used, the size available on the market, and the effect that different conditions could have on the metrological performances like, for example, overload conditions, flow rate pulsations, leakages through the clearance and pressure drops. Furthermore, different maintenance activities are usually expected, resulting in different operative costs throughout the lifetime. To date, turbine, rotary piston gas, and ultrasonic meters are used for fiscal gas metering in transmission networks. Specifically, based on the data collected, turbine gas meters are the most installed technologies for medium to high flow rate, followed by rotary piston and ultrasonic (for high flow rate). Few cases of use of Coriolis meters have been found. Regarding distribution, a different situation results. Despite the fact that few answers have been received to date, and only from Italy, it appears that diaphragm gas meters are the prevailing technology installed, even if a greater penetration is expected for thermal mass meters. THOTH2 also includes other measurements like gas quality by chromatographs, pressure and temperature, and trace water dew point. Regarding temperature, it was assumed that since the sensor is not in contact with the fluid but is protected by the thermowell, it can be assumed that no problem would arise. However, further investigation should be performed to investigate if any effect of hydrogen on response time exists. Regarding pressure measurement, many models are commercially available, but attention should be given to the effect of hydrogen on the material with which the fluid is in contact. Specifically, identifying critical materials that can be affected by hydrogen among those available in commercial products should be the next step to identifying the products to be tested. Gas chromatographs are also present in different models and configurations in the existing networks. Usually, different columns are used based on the specific analysis to be performed. Even if the range of the concentration allowed for each molecule is usually known for each model, more details about the configuration of each gas chromatograph are needed to complete the analysis and check the capability to handle hydrogen. Only some models of trace water sensors have been identified in the investigated networks. Specifically, impedance sensors result in the most implemented devices. Other devices are also typically used in the networks. Electronic Volume Converters and Flow Computers convert measurements into standardized gas volumes for fiscal purposes. The main issues to be investigated are the implemented algorithms and their capability to consider hydrogen. The main algorithms are AGA8, SGERG, and AGA-NX19, and the Operators can check the hydrogen limits. The main issue is that many different models are installed in gas transmission and distribution networks. Furthermore, based on the conclusion about pressure and temperature sensors, the potential effects of hydrogen on the metrological performances of those devices that have these sensors integrated have to be carefully assessed not to overcome the limits on errors provided by the standards. Last, leak detection is essential to detect fugitive emissions to the atmosphere and to minimize the risk of failures or accidents . To date, many devices are supplied to the technicians on the field to verify the presence of hazardous substances. Since different sensors can be implemented in the same devices to measure different quantities, attention should be given in Task 2.1 to selecting those sensors that, on the current knowledge, appear to be most critical when being in contact with hydrogen.</p>
SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages
<p>Supplementary information relating to the manuscript titled "SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages" that has been published on the preprint server arXiv.</p> <ul> <li>PersistentInfectionScore.nb: Mathematica code used to process the data and generate Figure 2</li> <li>PersistentInfectionScore.pdf: pdf version of the above file</li> <li>Supplementary_tables_Harari_et_al_2022.xlsx: raw data from (<a href="https://www.nature.com/articles/s41591-022-01882-4#Sec19">Harari et al. 2022</a>) that was used to generate mutation distributions</li> </ul>
Data from: Spatial distribution of the potential forest biomass availability in Europe
<p>European forests are considered a crucial resource for supplying biomass to a growing bio-economy in Europe. This study aimed to assess the potential availability of forest biomass from European forests and its spatial distribution. We tried to answer the questions (i) how is the potential forest biomass availability spatially distributed across Europe and (ii) where are hotspots of potential forest biomass availability located?</p> <p>The spatial distribution of woody biomass potentials was assessed for 2020 for stemwood, residues (branches and harvest losses) and stumps for 39 European countries. Using the European Forest Information SCENario (EFISCEN) model and international forest statistics, we estimated the theoretical amount of biomass that could be available based on the current and future development of the forest age-structure, growing stock and increment and forest management regimes. We combined these estimates with a set of environmental (site productivity, soil and water protection and biodiversity protection) and technical (recovery rate, soil bearing capacity) constraints, which reduced the amount of woody biomass that could potentially be available. We mapped the potential biomass availability at the level of administrative units and at the 10 km × 10 km grid level to gain insight into the spatial distribution of the woody biomass potentials.</p> <p>According to our results, the total availability of forest biomass ranges between 357 and 551 Tg dry matter per year. The largest potential supply of woody biomass per unit of land can be found in northern Europe (southern Finland and Sweden, Estonia and Latvia), central Europe (Austria, Czech Republic, and southern Germany), Slovenia, southwest France and Portugal. However, large parts of these potentials are already used to produce materials and energy. The distribution of biomass potentials that are currently unused only partially coincides with regions that currently have high levels of wood production.</p> <p>Our study shows how the forest biomass potentials are spatially distributed across the European continent, thereby providing insight into where policies could focus on an increase of the supply of woody biomass from forests. Future research on potential biomass availability from European forests should also consider to what extent forest owners would be willing to mobilise additional biomass from their forests and at what costs the estimated potentials could be mobilised.</p> <p>This dataset contains the data of the map presented in Figure 2A: Estimated spatial distribution of forest biomass availability according to the BASE potential (ton dry matter ha-1 land yr-1) at the grid (10x10 km) level.</p>
Chronological Distribution of the Documents from Yahudu and Its Surroundings
<p>This file presents the chronological distribution of the documents from the village of Yahudu and its surroundings in the Babylonian countryside. It relates to Chapter 4 in Tero Alstola, <em>Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE</em>. Culture and History of the Ancient Near East. Leiden: Brill. For further information, see the readme file.</p>
Github commit data for the article "Beyond Zipf's law: Exploring the discrete generalized beta distribution in open-source repositories"
<p><span>This dataframe corresponds to the data used in the Nowak's et al. 2024 article "Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories" (see reference below).</span></p> <p><span>It consists of the distirbutions of number of commits per user across a number of GitHub repositories. <br><br>There are three columns:</span></p> <ul> <li><span>repository: the repository name</span></li> <li><span># of commits: the number of commits of a given individual</span></li> <li><span>rank: the user rank in the repository (by decreasing number of commits)<br><br></span></li> </ul> <p><strong><span>Reference:</span></strong></p> <p><span>Nowak, P., Santolini, M., Singh, C., Siudem, G., & Tupikina, L. (2024). Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories. <em>Physica A: Statistical Mechanics and Its Applications</em>, <em>649</em>, 129927. <a href="https://doi.org/10.1016/j.physa.2024.129927">https://doi.org/10.1016/j.physa.2024.129927</a></span></p>
Multi-omics analysis reveals the link between Treg distribution and therapy efficacy in Hepatocellular Carcinoma patients treated with tremelimumab plus durvalumab
<p><strong><span><span>Introduction</span></span></strong></p> <p><span>Hepatocellular carcinoma (HCC) remains a significant contributor to cancer-related deaths. Immunotherapy, either alone or in combination, has emerged as the standard treatment for advanced HCC. Notably, the combination of durvalumab (dur) and tremelimumab (trem) has received FDA approval based on findings from the HIMALAYA trial. However, comprehensive studies elucidating immune responses are lacking. We conducted a thorough analysis utilizing clinical samples from tumor biopsies to understand the mechanism of response.</span></p> <p><strong><span><span>Methods</span></span></strong></p> <p><span>Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human FFPE samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 20 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms using the exported matrix of the marker expression and spatial information. Clinical metadata including sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples.</span></p> <p><strong><span><span>Results</span></span></strong></p> <p><span><span>Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC treated with immunotherapy. In situ phenotyping of 400,000 single cells (including 130,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers, and spatial features of each individual cell. This analysis revealed the comprehensive profile of the cell composition and spatial interactions of different cells in the TiME of patients treated with immunotherapy. Further details on the study can be obtained in our paper once it’s published.</span></span></p> <p><strong><span><span>Conclusion</span></span></strong></p> <p><span><span>We developed the CODEX panel for FFPE biopsy samples of HCC patients.</span></span></p>
Data: Dynamics of star clusters with tangentially anisotropic velocity distribution (Pavlik+ 2024)
<p>This dataset represents the results of our <em>N</em>-body simulations of star clusters (SCs). The initial conditions of the models are fully described in the referenced journal article. In short, the SCs start from isotropic, radially anisotropic or tangentially anisotropic initial velocity distributions, and each model is evolved in an external Galactic tidal field, for two different choices of the filling factor.</p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</p>
Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset
<p><span>The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022.<span> </span>Data were generated<span> </span>as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset. <br></span></p> <p><span>Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., <strong>Jaspers, C.</strong> (submitted) Biod</span><span>iversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. <em>Data in Brief. </em></span></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.