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Supplements for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras"
<p>Supplements for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras"</p>
Data for: Global frequency analyses of canine progressive rod-cone degeneration–progressive retinal atrophy and collie eye anomaly using commercial genetic testing data
<p>Hundreds of genetic variants associated with canine traits and disorders have been identified, with commercial tests offered. However, the geographic distributions and changes in allele and genotype frequencies over prolonged, continuous periods of time are lacking. This study utilized a large set of genotypes from dogs tested for the progressive rod-cone degeneration–progressive retinal atrophy (prcd-PRA) G>A missense PRCD variant (n = 86,667) and the collie eye anomaly (CEA)-associated NHEJ1 deletion (n = 33,834) provided by the commercial genetic testing company (Optigen/Wisdom Panel, Mars Petcare Science & Diagnostics). These data were analyzed using the chi-square goodness-of-fit test, time-trend graphical analysis, and regression modeling in order to evaluate how test results changed over time. The results span fifteen years, representing 82 countries and 67 breeds/breed mixes. Both diseases exhibited significant differences in genotype frequencies (p = 2.7 × 10−152 for prcd-PRA and 0.023 for CEA) with opposing graphical trends. Regression modeling showed time progression to significantly affect the odds of a dog being homozygous or heterozygous for either disease, as do variables including breed and breed popularity. This study shows that genetic testing informed breeding decisions to produce fewer affected dogs. However, the presence of dogs homozygous for the disease variant, especially for prcd-PRA, was still observed fourteen years after test availability, potentially due to crosses of unknown carriers. This suggests that genetic testing of dog populations should continue.</p>
Hand contamination and hand hygiene knowledge and practices among commercial transport users, Enugu State, Nigeria
<p>Hand hygiene practice (HHP) and hand contamination as a critical component of infection prevention and control were investigated among commercial drivers and passengers <span>in Nsukk and Enugu towns </span>to determine their knowledge attitude/belief and practices regarding hand hygiene and the degree of hand contamination<span>n</span>. The study used a descriptive cross-sectional questionnaire survey to gather data samples from 600 transports<span> from ten commercial transport parks</span>. Also 100 hand swabswere taken to examine the dominant hand contamination levels. The data from questionnaire response, and percentage bacterial and fungal species contamination by motor parks are provided here.</p>
FIGURE 1 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 1 Area of study in the middle Paraná River, Argentina. Numbers (Cod) indicate ports where data of commercial fisheries were obtained: Producción Pesquera Argentina (the Argentine inland fishery production; PPARG) 1–2; 7–8; 10 and Ministerio de Producción (Ministry of Production), Subdirección General de Ecología de Santa Fe, Sub-directorate General of Ecology of Santa Fe Province (PPSF 3–6; 9). LH: Laboratory of Hydroecology. INALI: Argentine National Institute of Limnology. fd: flow direction. (Modified from Rabuffetti et al., 2016)
FIGURE 6 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 6 Interannual fish abundance and size structure of landed and measured fishes (total and per species) for distinct hydroclimatic conditions. (), monthly mean water level (Hmax). (), mean monthly temperature; (), total number of individuals collected (n); (), number of individual measured (standard length Ls). () size 1-10, () size 10-20, () size 20-30, () size 30-40
FIGURE 5 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 5 (a, b) Interannual maximum water levels (Hmax) at Santa Fe Port gauge () and fluctuation of Prochilodus lineatus catches by institution datasets in the middle Paraná River 1935–2016. (c) catches per decade from PPARG and (d) INALI datasets. (), Total catch; (), adults and pre-adults; (), Young-of-the-year;, spring–summer flood events (> 4.5 m at Sante Fe Port gauge). PPARG, Producción Pesquera Argentina; PPSF, Ministerio de Producción, Sub-directorate General of Ecology of Santa Fe Province; INALI, Argentine National Institute of Limnology; LH, Laboratory of Hydroecology
FIGURE 3 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 3 (a) Maximum water levels (Hmax) and duration of floods when water level was>4.5 m at Sante Fe Port guage between 1905 and 2016. (b) Correlation between Hmax and duration of floods considering only years with biological information of fishes., Spring summer flood (SSF) events are highlighted in red
FIGURE 7 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 7 Association of sizes range of landed and measured commercial fishes with the most significant hydroclimatic variables during different hydrological conditions recorded along the studied period. (a) Argentine National Institute of Limnology (INALI) 1965–1967, 1978–1979, 1984–1985, 1992–1994; (b) Laboratory of Hydroecology (LH) (2009–2011, 2013–2015)
FIGURE 2 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 2 Cluster analysis of flow regime in the study period (1920–2016) using 18 hydroclimatic variables; spring summer flood (SSF) events are highlighted in red
FIGURE 8 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 8 Schematic representation of interannual variation of maximum water levels (Hmax; 1935–2016) showing the influence on fish catches of hydroclimatic fluctuations (humid–dry periods). Note higher fish catches after largest spring– summer flood (SSF; ()) events (Hmax> 4.5 m at Sate Fe Port gauge). Probability of anthropic influence was indicated with lines of distinct thickness. WL: water level. () Higher fish catches, () El Nino (ENSO), () Historial disconnection event, () Larger SpringSummerFlood
FIGURE 4 in Commercial fisheries in a mega unregulated floodplain river: Assessment of the most favourable hydrological conditions for its preservation
FIGURE 4 Distance-based redundancy analysis (dbRDA) of year-to-year differences in commercial fish catches in the middle Paraná River from 1935 to 2016 explained and structured by flow regime variables: fisheries data from (a) PPARG, Producción Pesquera Argentina; (b) PPSF, Ministerio de Producción, Sub-directorate General of Ecology of Santa Fe Province; (c) INALI, Argentine National Institute of Limnology; (d) LH, Laboratory of Hydroecology
Pesticide risk during commercial apple pollination is greater for honeybees than other managed and wild bees
<p>Data and code relating to the manuscript “Pesticide risk during commercial apple pollination is greater for honeybees than other managed and wild bees”</p> <p>Files are organized as follows</p> <p><strong>input</strong> - contains the main data files.</p> <ul> <li> <p>all_pesticide2019.csv – contains the pesticide residue data for all samples</p> </li> <li> <p>LD50.csv – contains pesticide LD50s in PPB</p> </li> <li> <p>ld50_per_bee.csv – contains pesticide LD50s in ug / honeybee</p> </li> <li> <p>nesting_type.csv – lists nesting types of the different bee samples</p> </li> <li> <p>pesticide_type.csv – lists pesticides by their type</p> </li> <li> <p>short_name.csv – lists short bee names</p> <p><strong>folder “gis”</strong></p> <ul> <li>hive_distance_matrix.csv – distances from each orchard to other sites</li> <li>sitelocation.csv – contains coordinates of sites</li> </ul> </li> </ul> <p><strong>code</strong> - contains the scripts to analyze the input files</p> <ul> <li>pesticide_analysis.R – is the main anaylsis for the paper</li> <li>unadjusted_pesticide_analysis.R – is a copy of most of the code above but without LD50 weight adjustements</li> </ul> <p><strong>ld50 adjust</strong> - contains the inputs and code for the ld50 adjustments I ran</p> <ul> <li>ld50_adjust.R – is the code to calculate our the adjustements</li> <li>2020_BeeTox_database_acute_contact_publication_final_R1.csv – is the data file taken from pamminger publication</li> </ul>
Polypropylene and polystyrene may be less thrombogenic than a commercial non-binding surface under plasma-free conditions
<p>Trained Ilastik model used to quantification of survace coverage by platelets is included along with python scripts and ImageJ macros.</p> <p>Raw data representing platelet coverage are inclued in xls file.</p>
Contrasting clonal and population genetic structure in two endangered Costa Rican Vanilla species of commercial interest
Open the record for dataset details and reuse information.
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.
Dataset for modeling distribution of commercial fish and shrimps
<p>This dataset show output of MaxEnt for modeling distribution of commercial fish and shrimps </p>
FIGURE 6 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori
FIGURE 6. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 28S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.
FIGURE 3 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori
FIGURE 3. Distribution of 28S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids.
FIGURE 5 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori
FIGURE 5. BI phylogenetic analysis for H. polii, H. sanctori, and different Mediterranean holothuroids is based on partial sequences of the 16S rDNA gene. Four Markov Chains Monte Carlo (MCMC) samples were analyzed for 10 million (ngen=10,000,000) generations. Bootstrap values are shown above the branches. The abbreviation "Hap" denotes haplotype. Bold labels refer to sequences obtained in the current study for H. polii and H. sanctori.
FIGURE 2 in Comparing genetic markers' efficiencies for discrimination between two commercially important holothuroids in the Mediterranean Sea, Holothuria polii and Holothuria sanctori
FIGURE 2. Distribution of 16S rDNA-based genetic pairwise distances among different Mediterranean species obtained in the current study and available in the GenBank. (a): H. polii vs. Mediterranean holothuroids, (b) H. sanctori vs. the Mediterranean holothuroids. For the 28S rDNA, the genetic distance for H. polii was the lowest with H. forskali (0.01) but the highest with H. sanctori (1.08) (Fig. 2A). For H. sanctori, the lowest pairwise distance was with H. polii (1.08), while the highest was with P. longicauda (1.163) (Fig. 2B).
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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.