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Figure 10 A in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 10 A final, picked water mite sample preserved in 80% ethanol ready for identification and enumeration.
Figure 7 After sieving, the collector removes the contents from the 250 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 7 After sieving, the collector removes the contents from the 250 μm sieve and places them into the final sample jar. a – fine sediment
Figure 4 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 4 An example of the collection process proposed in this manuscript. a – the collector stands upstream of the net and disturbs the substrate; b – a typical collection setup that includes one person shoveling and another holding the net; c – water mites become suspended in the water column and flow downstream into the net.
Figure 9 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 9 The picking process proposed in this manuscript. a – the picker empties the contents of the sample container into the white photographic
Simulation code for: Clones on the run - the genomics of a recently expanded partially clonal species
<p class="MsoNormal"><span>Why species that in their core areas mainly reproduce sexually become enriched with clones in marginal populations ("geographic parthenogenesis") remains unclear. Earlier hypotheses have emphasised that selection might promote clonality because it protects locally adapted genotypes. On the other hand, it also hampers recombination and adaptation to changing conditions. The aim of the present study was to investigate the early stages of range expansion in a partially clonal species and what drives an increase in cloning during such expansion. We used genome-wide sequencing to investigate the origin and evolution of large clones formed in a macroalgal species (<em>Fucus vesiculosus</em>) during a recent expansion into the postglacial Baltic Sea. We found low but persistent clonality in core populations, while at range margins, large dominant clonal lineages had evolved repeatedly from different sexual populations. A range expansion model showed that even when asexual recruitment is less favourable than sexual recruitment in core populations, repeated bottlenecks at the expansion front can establish a genetically eroded clonal wave that spreads ahead of a sexual wave into the new area. Genetic variation decreased by drift following repeated bottlenecks at the expansion front. This resulted in the emerging clones having low expected heterozygosity, which corroborated our empirical observations. We conclude that Baker's Law (clones being favoured by uniparental reproductive assurance in new areas) can play an important role during range expansions in partially clonal species, resulting in a complex spatiotemporal mosaic of clonal and sexual lineages that might persist during thousands of generations. </span></p>
Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES
<p>Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. </p> <p> </p> <p>This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1).</p> <p> </p> <p>Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.</p> <p> </p>
Figure 3 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 3. Multivariate generalized linear mixed-effects model results showing correlations (see coloured scale for total range) of different dispersal modes among species.
Figure 2 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 2. Multivariate generalized linear mixed-effects model results showing joint effects of feeding strategies on dispersal modes in mites. Posterior means are given (dots), along with 50% (thick lines) and 95% (thin lines) credible intervals for Acariformes (orange) and Parasitiformes (violet). If the 95% credible intervals do not cover zero (intense colours), the particular dispersal mode is significantly more frequent or less frequent than expected by chance (at the α = 0.05 level).
Figure 4 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 4. Relationship between the number of studies carried out on the dispersal syndrome of a given mite species and the proportion of dispersal modes revealed. The shaded region depicts 95% confidential intervals around the fit. Points are observed data points. Note the logarithmic scale on both axes.
Figure 1 in Do mites eat and run? A systematic review of feeding and dispersal strategies
Figure 1. Database collection summary. PRISMA (preferred reporting items for systematic reviews and meta-analyses) diagram detailing the procedure for the identification and inclusion of relevant publications.
Opportunities and Limitations of Running Python Code in the Web Browser
<p>Python is a popular programming language that is widely used for a variety of applications such as web development, data analysis, scientific computing, and education. This versatility makes it a popular choice for developers and educators who need a language that can be used for a wide variety of tasks. While Python is typically run on the server side or on the desktop, there is a growing interest in running Python code directly on the client side, i.e., in the browser.</p> <p>There are several possibilities available for running Python code in the browser, among others,</p> <ul> <li>transpiling it into JavaScript (cf. e.g., Transcrypt),</li> <li>running it by making use of an interpreter implemented in JavaScript (cf. e.g., Brython or Skulpt),</li> <li>or executed it by leveraging a Python interpreter compiled to WebAssembly (cf. e.g., Pyodide or CoWasm), an open standard defined by the World Wide Web Consortium specifying a bytecode for running programs in browsers.</li> </ul> <p>Since the choice of the right tool for a given application depends on the specific requirements and constraints, the aim of this bachelor thesis is to provide an overview of the state of the art in running Python code in the browser. This is done by presenting a variety of different tools and environments available, their capabilities and limitations, as well as some application examples and sample code. Furthermore, similar to Kiyokawa’s & Jin’s (2022) work on “A Front-End Framework Selection Assistance System [...]”, the objective of this bachelor’s thesis is to develop criteria to help developers and educators in the selection process.</p> <p> </p> <p>Link to Respository: <a href="https://git.uibk.ac.at/csav4362/running-python-in-web-browser">https://git.uibk.ac.at/csav4362/running-python-in-web-browser</a></p>
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 0-5 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/388acf49-4cd6-4b32-9abb-e0f343c48cfd. Produced on QA4SM (https://qa4sm.eu)
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 5-10 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/5219f9fd-d309-4405-befb-cf829f695bee. Produced on QA4SM (https://qa4sm.eu)
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (absolute) 5-10 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/6a33bb0a-7744-4950-8478-d55b234d4b29. Produced on QA4SM (https://qa4sm.eu)
Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (absolute) 0-5 cm
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/44c5ab62-c46e-4ebb-b99d-0bbf33e8b17f. Produced on QA4SM (https://qa4sm.eu)
Рис. 1. ПреΔпочтитеΛьное отношение паукообразных (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) к объектам питания по способу переΔвижения жертвы: I – Λетающие; II – прыгающие; III – бегающие; IV – хоΔящие; V – поΛзающие; VI – маΛопоΔвижные; VII – непоΔвижные. Fig. 1. Preferred attitude of arachnids (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) to food objects by the method of movement of pray: I – flying; II – jumping; III – running; IV – walking; V – crawling; VI – sluggish; VII – motionless. in Comparison of trophic spectra and hunting strategies of some large arachnids (Arachnida: Scorpiones, Solifugae, Aranei) in semi-desert biocenoses of Gobustan (Eastern Azerbaijan)
Рис. 1. ПреΔпочтитеΛьное отношение паукообразных (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) к объектам питания по способу переΔвижения жертвы: I – Λетающие; II – прыгающие; III – бегающие; IV – хоΔящие; V – поΛзающие; VI – маΛопоΔвижные; VII – непоΔвижные. Fig. 1. Preferred attitude of arachnids (1 – Galeodes araneoides, 2 – Lycosa praegrandis, 3 – Mesobuthus eupeus) to food objects by the method of movement of pray: I – flying; II – jumping; III – running; IV – walking; V – crawling; VI – sluggish; VII – motionless.
Input data for running a GWAS on penicillin resistance in Streptococcus pneumoniae
<p>Results from running the pyseer tutorial at https://pyseer.readthedocs.io/en/master/tutorial.html</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Results and log of LLM-KG-Bench runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al. 2023
<p>Results and logs of <a href="https://github.com/AKSW/LLM-KG-Bench">LLM-KG-Bench</a> runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al., to appear in <a href="https://2023-eu.semantics.cc/page/accepted_posters">SEMANTICS 2023 poster track</a> proceedings.</p>
3D CAD models exemples to run "ArtificialReef_Complexity" Python script (STL files)
<p>Here you will find 3D CAD models in STL format.</p> <p>These are 3D CAD models of fractal pyramid.</p> <p>These STL files can be used as an example to run the Python script "ArtificialReef_Complexity: v.1.3" available on GitHub (<a href="https://github.com/ELI-RIERA/ArtificialReef_Complexity/tree/V1.3">https://github.com/ELI-RIERA/ArtificialReef_Complexity/tree/V1.3</a>)</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.