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Electron number density, temperature, gas mass and hydrostatic mass profiles for 22 eROSITA clusters
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Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics (output data)
<p>This contains the output results of CalicoST (inferred CNAs and cancer clones), results of comparison methods, and CNAs inferred from WES data of 13 samples across four cancer types.</p> <p>In this updated version, we also included the simulated data and the results from CalicoST and other methods in CalicoST_simulation_deposit.zip. README contains the details of deposited files.</p>
Number of N. ceranae spores in the honeybees from groups 1 and 2
<p>This file contain raw results from Figures 1-2 and Table 5 (Kunat-Budzyńska M., Łabuć E., Ptaszyńska A.A. 2025, Seasonal detection of pathogens in honeybees kept in natural and laboratory conditions, Parasitology International, 1383-5769, doi: 10.1016/j.parint.2024.102978). The work was financed by the National Science Centre, Poland, project number 2021/41/N/NZ6/00735.</p>
Distribution. Sub-Saharan Africa; virtually eradicated from W Africa, and greatly reduced in C and NE Africa. The largest populations exist in Botswana, Tanzania, and Zimbabwe, which account for approximately half of the estimated number of African Wild Dogs remaining in the wild. Other populations occur in Central African Republic, Ethiopia, Kenya, Mozambique, Namibia, South Africa, Sudan, and Zambia. Potential small populations (less than 100 individuals) may exist in Cameroon, Chad, Senegal, and Somalia. in Canidae
Distribution. Sub-Saharan Africa; virtually eradicated from W Africa, and greatly reduced in C and NE Africa. The largest populations exist in Botswana, Tanzania, and Zimbabwe, which account for approximately half of the estimated number of African Wild Dogs remaining in the wild. Other populations occur in Central African Republic, Ethiopia, Kenya, Mozambique, Namibia, South Africa, Sudan, and Zambia. Potential small populations (less than 100 individuals) may exist in Cameroon, Chad, Senegal, and Somalia.
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest. in Suidae
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest.
Distribution. Sulawesi and adjacent Is (Buton, Kabaena, Muna, Peleng, Lembeh, and on some of the Togian Is); thought to be extinct on Selayar I. Pigs have been widely domesticated through the Indonesian archipelago and beyond. This primarily involved the Eurasian Wild Pig (S. scrofa), but also S. celebensis, the only other species of pig successfully domesticated. Mitochondrial DNA studies of the dispersion of these domesticated forms agree on three major dispersal events, two involving S. scrofa and one S. celebensis. Evidence supports an early human-mediated translocation of S. celebensis to Flores and Timor and two later, separate human-mediated dispersals of domestic pig through islands of SE Asia into Oceania. In addition to Flores and Timor, S. celebensis is also thought to occur in its domesticated form on Halmahera, Lendu, Roti, and Savur Is, and even on Simeulue and Nias Is to the W of Sumatra and far from its island of origin, Sulawesi. In the Moluccas, and possibly elsewhere in this region, introduced S. celebensis are thought to have hybridized with other introduced pigs of S. scrofa derivation, and apparent hybrids between these species are now reported to survive on a number of islands, including Salawatti, Great Kei, Dobu, Seram, Ambon, Bacan, Ternate, Morotai, and New Guinea. It is also reported that in the 19" century the sows of domestic pigs in Sulawesi frequently mated with wild animals, after which they returned to their villages. in Suidae
Distribution. Sulawesi and adjacent Is (Buton, Kabaena, Muna, Peleng, Lembeh, and on some of the Togian Is); thought to be extinct on Selayar I. Pigs have been widely domesticated through the Indonesian archipelago and beyond. This primarily involved the Eurasian Wild Pig (S. scrofa), but also S. celebensis, the only other species of pig successfully domesticated. Mitochondrial DNA studies of the dispersion of these domesticated forms agree on three major dispersal events, two involving S. scrofa and one S. celebensis. Evidence supports an early human-mediated translocation of S. celebensis to Flores and Timor and two later, separate human-mediated dispersals of domestic pig through islands of SE Asia into Oceania. In addition to Flores and Timor, S. celebensis is also thought to occur in its domesticated form on Halmahera, Lendu, Roti, and Savur Is, and even on Simeulue and Nias Is to the W of Sumatra and far from its island of origin, Sulawesi. In the Moluccas, and possibly elsewhere in this region, introduced S. celebensis are thought to have hybridized with other introduced pigs of S. scrofa derivation, and apparent hybrids between these species are now reported to survive on a number of islands, including Salawatti, Great Kei, Dobu, Seram, Ambon, Bacan, Ternate, Morotai, and New Guinea. It is also reported that in the 19" century the sows of domestic pigs in Sulawesi frequently mated with wild animals, after which they returned to their villages.
FIGURE Phylogenetic relationships of the Coelastrella genus inferred from the 18S-ITS1-5.8S-ITS2 region. The Neighbor-Joining (NJ), Maximum Likelihood (ML) bootstrap values and Bayesian posterior probabilities (PP) are presented at the nodes (NJ/ML/PP). Only values above 75 are shown. Strains provided in this study are indicated in bold font. Authentic strains marked with asterisks. The scale bar represents the number of substitutions per site. The GenBank accession numbers of Coelastrella can be found in the Table 3. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE Phylogenetic relationships of the Coelastrella genus inferred from the 18S-ITS1-5.8S-ITS2 region. The Neighbor-Joining (NJ), Maximum Likelihood (ML) bootstrap values and Bayesian posterior probabilities (PP) are presented at the nodes (NJ/ML/PP). Only values above 75 are shown. Strains provided in this study are indicated in bold font. Authentic strains marked with asterisks. The scale bar represents the number of substitutions per site. The GenBank accession numbers of Coelastrella can be found in the Table 3.
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined.
Random numbers
This is a collection of ten datasets, each containing ten iid zero-mean, unit-variance random numbers. These are used in an example of the showyourwork open source scientific article workflow.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/rodluger/showyourwork-template/tree/d24bc06388bd4be5038b2eef6dbc681d9d786ecf">this GitHub repo</a> using the following command:<br><br><pre><code class="language-bash">cd src/figures && python src/analysis/run_simulation.py</code></pre><br>
Input data for estimating dimensionless number (Reynolds, Swimming and Strouhal number) of swimming penguin
<p>Propulsion performance of swimming and flying animals is often evaluated by using dimensionless numbers, such as the Strouhal and Reynolds numbers. They have been shown to allow better understanding of locomotion efficiency, using relatively simple approaches and avoiding overly complex computational models. Specifically, it has been reported that efficient propulsion is more likely to occur when Strouhal number values – estimated from propulsive frequencies and amplitudes – are within a relatively narrow range, depending on the corresponding Reynolds number, broadly expressing the fluid resistance to the animal motion. We have estimated both Strouhal and Reynolds numbers for seven species of penguins after analysing relevant kinematic data taken from the literature. The obtained values neatly indicate that, as expected, penguins employ efficient propulsion mechanisms. Additionally, by comparing these values with those for alcids – seabirds that can also fly – we have found that penguins swim at least as efficiently as alcids. However, we have also found that the swimming number – proportional to the product of Strouhal and Reynolds numbers – neatly correlates to the diving abilities of the considered species and apparently indicates, in a straightforward hierarchical manner, the gains in diving due to the loss of flying abilities. Within the penguin species, a clear correlation is also observed between diving performance and drag coefficient values.</p>
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known. in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known.
Number of growth days and not length of the growth period determines radial stem growth of temperate trees
<p>Radial stem growth dynamics at seasonal resolution are essential to understand how forests respond to climate change. We studied daily radial growth of 160 individuals of seven temperate tree species at 47 sites across Switzerland over eight years. Growth of all species peaked in the early part of the growth season and commenced shortly before the summer solstice, but with species-specific seasonal patterns. Day length set a window of opportunity for radial growth. Within this window, the probability of daily growth was constrained particularly by air and soil moisture, resulting in intermittent growth to occur only on 29 to 77 days (30 to 80 %) within the growth period. The number of days with growth largely determined annual growth, whereas the growth period length contributed less. We call for accounting these non-linear intra-annual and species-specific growth dynamics in tree and forest models to reduce uncertainties in predictions under climate change.</p>
Genome-wide analysis of natural and restored eastern oyster populations reveals local adaptation and positive impacts of planting frequency and broodstock number
<p>The release of captive-bred plants and animals has increased worldwide to augment declining species. However, insufficient attention has been given to understanding how neutral and adaptive genetic variation are partitioned within and among proximal natural populations, and the patterns and drivers of gene flow over small spatial scales, which can be important for restoration success. A seascape genomics approach was used to investigate population structure, local adaptation, and the extent to which environmental gradients influence genetic variation among natural and restored populations of Chesapeake Bay eastern oysters <i>Crassostrea virginica</i>. We also investigated the impact of hatchery practices on neutral genetic diversity of restored reefs and quantified the broader genetic impacts of large-scale hatchery-based bivalve restoration. Restored reefs showed similar levels of diversity as natural reefs, and striking relationships were found between planting frequency and broodstock numbers and genetic diversity metrics (effective population size and relatedness), suggesting that hatchery practices can have a major impact on diversity. Despite long-term restoration activities, haphazard historical translocations, and high dispersal potential of larvae that could homogenize allele frequencies among populations, moderate neutral population genetic structure was uncovered. Moreover, environmental factors, namely salinity, pH, and temperature, play a major role in the distribution of neutral and adaptive genetic variation. For marine invertebrates in heterogeneous seascapes, collecting broodstock from large populations experiencing similar environments to candidate sites may provide the most appropriate sources for restoration and ensure population resilience in the face of rapid environmental change. This is one of a few studies to demonstrate empirically that hatchery practices have a major impact on the retention of genetic diversity. Overall, these results contribute to the growing body of evidence for fine-scale genetic structure and local adaptation in broadcast-spawning marine species and provide novel information for the management of an important fisheries resource.</p>
Number of Water Collection, Treatment and Supply Enterprises
<p>An imputed dataset based on the Eurostat database Business demography by size class (from 2004 onwards, NACE Rev. 2)</p>
Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice
<p>To understand the genetic basis of main culm panicle node number, maximum node production rate, and degree-days to heading in rice (Oryza sativa), we conducted genome-wide association studies using a diversity panel of 220 rice accessions and 854,832 SNP markers generated using genotyping-by-sequencing (GBS), with 1X coverage. The raw genotype data was filtered, selecting single nucleotide polymorphisms (SNPs) having less than 50% missing data and minimum allele frequency (MAF) >5%. After initial filtering, imputation was conducted using BEAGLE V4.0 in 1,075,302 SNP markers. After imputation, the dataset was filtered a second time by removing SNPs with less than 5% MAF and more than 5% missing data. A total of 854,832 SNPs were used in the genome-wide association analyses. The dataset representing the genotype data of 854,832 SNP markers by 220 rice accessions is presented here.</p>
Get in touch with numbers – data
<p>Dataset</p>
Influence of number of individuals and observations per individual on a model of community structure
<p>Social network analysis is increasingly applied to understand animal groups. However, it is rarely feasible to observe every interaction among all individuals in natural populations. Studies have assessed how missing information affects estimates of individual network positions, but less attention has been paid to metrics that characterize overall network structure such as modularity, clustering coefficient, and density. In cases such as groups displaying fission-fusion dynamics, where subgroups break apart and rejoin in changing conformations, missing information may affect estimates of global network structure differently than in groups with distinctly separated communities due to the influence single individuals can have on the connectivity of the network. Using a bat maternity group showing fission-fusion dynamics, we quantify the effect of missing data on global network measures including community detection. In our system, estimating the number of communities was less reliable than detecting community structure. Further, reliably assorting individual bats into communities required fewer individuals and fewer observations per individual than to estimate the number of communities. Specifically, our metrics of global network structure (i.e., graph density, clustering coefficient, R<sub>com</sub>) approached the 'real' values with increasing numbers of observations per individual and, as the number of individuals included increased, the variance in these estimates decreased. Similar to previous studies, we recommend that more observations per individual should be prioritized over including more individuals when resources are limited. We recommend caution when making conclusions about animal social networks when a substantial number of individuals or observations are missing, and when possible, suggest subsampling large datasets to observe how estimates are influenced by sampling intensity. Our study serves as an example of the reliability, or lack thereof, of global network measures with missing information, but further work is needed to determine how estimates will vary with different data collection methods, network structures, and sampling periods.</p>
Code for JAMES publication - Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme.
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the JAMES publication "Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme"</p> <p> </p> <p>The revised WDM6 code, which predicts the number concentration of cloud ice, and the original WDM6 code are uploaded. Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p> </p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p>Scripts file for figures in this manuscript are uploaded</p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 36 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 36\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong><span class="math-tex">\(v_s\)</span></strong></td> <td><strong><span class="math-tex">\(B\)</span></strong></td> </tr> <tr> <td>S1</td> <td>5.24</td> <td>10</td> </tr> <tr> <td>S2</td> <td>0</td> <td>0</td> </tr> <tr> <td>S3</td> <td>5.24</td> <td>30</td> </tr> <tr> <td>S4</td> <td>5.24</td> <td>50</td> </tr> <tr> <td>S5</td> <td>2.62</td> <td>10</td> </tr> <tr> <td>S6</td> <td>0.53</td> <td>10</td> </tr> <tr> <td>S7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>S8</td> <td>4.18</td> <td>10</td> </tr> <tr> <td>S9</td> <td>6.80</td> <td>10</td> </tr> <tr> <td>S10</td> <td>15.47</td> <td>10</td> </tr> <tr> <td>S11</td> <td>5.24</td> <td>20</td> </tr> <tr> <td>S12</td> <td>13.08</td> <td>10</td> </tr> <tr> <td>S13</td> <td>8.25</td> <td>10</td> </tr> <tr> <td>S14</td> <td>9.49</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table>
Data for sperm numbers as a paternity guard in a wild bird
Sperm competition is thought to impose strong selection on males to produce competitive ejaculates to outcompete rival males under competitive mating conditions. Our understanding of how different sperm traits influence fertilization success, however, remains limited, especially in wild populations. Recent literature highlights the importance of incorporating multiple ejaculate traits and pre-copulatory sexually selected traits in analyses aimed at understanding how selection acts on sperm traits. However, variation in a male's ability to gain fertilization success may also depend upon a range of social and ecological factors that determine the opportunity for mating events both within and outside of the social pair-bond. Here, we test for an effect of sperm quantity and sperm size on male reproductive success in the red-back fairy-wren (Malurus melanocephalus) while simultaneously accounting for pre-copulatory sexual selection and potential socio-ecological correlates of male mating success. We found that sperm number (i.e., cloacal protuberance volume), but not sperm morphology, was associated with reproductive success in male red-backed fairy-wrens. Most notably, males with large numbers of sperm available for copulation achieved greater within-pair paternity success. Our results suggest that males use large sperm numbers as a defensive strategy to guard within-pair paternity success in a system where there is a high risk of sperm competition and female control of copulation. Finally, our work highlights the importance of accounting for socio-ecological factors that may influence male mating opportunities when examining the role of sperm traits in determining male reproductive success.
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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.