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FIGURE 1 in Larvae of Hydropsyche angustipennis (Trichoptera, Hydropsychidae) as indicators of stream contamination by heavy metals in Łódź agglomeration
FIGURE 1. Study area with sampling sites.
FIGURE 4 in Larvae of Hydropsyche angustipennis (Trichoptera, Hydropsychidae) as indicators of stream contamination by heavy metals in Łódź agglomeration
FIGURE 4. Annual content of heavy metals in larval bodies at given sampling sites.
FIGURE 3 in Larvae of Hydropsyche angustipennis (Trichoptera, Hydropsychidae) as indicators of stream contamination by heavy metals in Łódź agglomeration
FIGURE 3. Hierarchical cluster analysis for abiotic and biotic parameters in given sampling sites.
2015 Academic Librarians' Familiarity with Research Impact Indicators at R1 Institutions
<p>This data is from a national survey conducted during 2015, which was administered to 13,436 full-time academic librarians from 150 institutions included in the 2013 National Center for Education Statistics (NCES) list of Carnegie-classified R1 institutions in the United States. Email addresses were collected manually in order to filter out certain positions not considered professional in nature and the survey was designed to eliminate responses from outside of the US, as well as part-time, retired, and other non-full-time respondents. The Qualtrics-administered survey was sent out three times over a two-week period in 2015, resulting in a 5.3% response rate (n=707, valid responses counted only).</p> <p>The survey's purpose was to assess the levels of familiarity, the levels of frequency for different types of uses, and the purposes of those uses of research impact metrics, usage data, and qualitative measures of research impact among academic librarians at R1 institutions in the U.S.</p>
Petty Cash: Database + Indicators
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DISTINCTIONS BETWEEN BIOCHEMICAL INDICATORS OF DIFFERENT BLOOD GROUPS
<p>The purpose of this work is to evaluate the differences in some biochemical parameters in blood plasma of blood groups I (0), II (A), III (B) and IV (AB). Biochemical indicators were determined on a Humastar100 (Germany) biochemical analyzer. The obtained results showed that there are certain statistically reliable differences between the amounts of biochemical indicators such as albumin, glucose, cholesterol, triglyceride and urea in blood plasmas of different blood groups.</p>
PHOSPHORUS FERTILIZERS AND MORPHOLOGICAL INDICATORS OF CORN
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OBJECTIVE INDICATORS OF NONCOMPLIANCE WITH SAFETY REGULATIONS FOR VEHICLE MOVEMENT OR UTILIZATION
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Intercropping indices evaluation on grain legume-small grain cereals mixture: a critical meta-analysis review
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BioClim Austria: Gridded climate indicators for 1961-1990 and 1991-2020 at 250m resolution
<h2>Overview</h2> <p>This gridded meteorological data set consists of climatologies (climate indicators) on a 30-year average basis for Austria and covers two historical periods with a high spatial resolution of 250m. The two 30-year periods provided for the observations allow the analysis of the climate change that has already occurred. The selection of climate indicators is optimized for the needs of ecological models. </p> <p><strong>Resolution</strong>: 250x250m<br><strong>Projection</strong>: EPSG 31287<br><strong>Extent</strong>: Austria<br><strong>Periods</strong>: 1961-1990 and 1991-2020<br><strong>Format:</strong> GeoTIFF<br><strong>Data sources:</strong> Station data and derived products</p> <p><strong>List of climatologies (climate indicators) </strong></p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y), monthly (M) or growing season (GS)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>8</em></p> </td> <td> <p>thermal_continentality</p> </td> <td> <p>Average annual amplitude of monthly mean temperature (Thermal continentality)</p> </td> <td> <p>Climatological monthly mean temperature in the warmest month minus climatological monthly mean temperature in the coldest month (04_tas* minus 05_tas*). </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>9</em></p> </td> <td> <p>FD</p> </td> <td> <p>Average number of frost days per year</p> </td> <td> <p>Defined by 0°C daily minimum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>11</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature in the growing season</p> </td> <td> <p>Average temperature in the growing season defined as in climate indicator 10 (GSL). If a year does not have a growing season, this value is not defined. </p> </td> <td> <p>°C</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>15</em></p> </td> <td> <p>HD35</p> </td> <td> <p>Average number of extremely hot days above 35°C per year</p> </td> <td> <p>Defined by 35°C daily maximum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>16</em></p> </td> <td> <p>FD_10</p> </td> <td> <p>Average number of days with hard frost below -10°C per year</p> </td> <td> <p>Defined by -10°C daily minimum temperature at 2m height.</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation on horizontal surface</p> </td> <td> <p>Also called irradiance or shortwave incoming radiation, taking cloud cover into account.</p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>21</em></p> </td> <td> <p>GLO_real</p> </td> <td> <p>Average sum of global radiation on the real surface</p> </td> <td> <p>Also called irradiance or shortwave incoming radiation, taking cloud cover into account.</p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>31</em></p> </td> <td> <p>vpd</p> </td> <td> <p>Average water vapor pressure deficit</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>hPa</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>31</em></p> </td> <td> <p>hurs</p> </td> <td> <p>Average relative humidity</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>%</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>32</em></p> </td> <td> <p>vpd</p> </td> <td> <p>Average water vapor pressure deficit</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>hPa</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>32</em></p> </td> <td> <p>hurs</p> </td> <td> <p>Average relative humidity</p> </td> <td> <p>Calculated from daily dew point temperature and temperature. </p> </td> <td> <p>%</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>34</em></p> </td> <td> <p>hygric_continentality</p> </td> <td> <p>Average hygric continentality according to Gams</p> </td> <td> <p>Defined as arctan of (elevation/annual_precip). Gams, H. (1931). Die klimatische Begrenzung von Pflanzenarealen und die Verteilung der hygrischen Kontinentalität in den Alpen. </p> </td> <td> <p>°</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>35</em></p> </td> <td> <p>pr1mm</p> </td> <td> <p>Average number of days with precipitation </p> </td> <td> <p>Daily precipitation of at least 1mm. </p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>36</em></p> </td> <td> <p>pr1mm</p> </td> <td> <p>Average number of days with precipitation in the growing season</p> </td> <td> <p>Daily precipitation of at least 1mm. </p> </td> <td> <p>days</p> </td> <td> <p>GS</p> </td> </tr> <tr> <td> <p><em>37</em></p> </td> <td> <p>DP_3days, DP_5days, DP_7days</p> </td> <td> <p>Average number of days in dry periods in summer half-year</p> </td> <td> <p>Number of days in periods of at least 3, 5, or 7 days with a daily precipitation total of less than 1mm. Summer half-year: April to September</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>41</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance in the growing season</p> </td> <td> <p>Precipitation minus potential evapotranspiration in the growing season</p> </td> <td> <p>mm</p> </td> <td> <p>GS</p> </td> </tr> </tbody> </table> <p> </p> <h2>Data sources</h2> <p>The climate indicators were calculated using daily data from different sources:</p> <ul> <li><strong>Temperature</strong>: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-015-1411-4 )</li> <li><strong>Precipitation</strong>: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-017-2093-x )</li> <li><strong>Radiation</strong>: <br>APOLIS SHORT(Gridded data set, 100x100m, 2006-2020, https://adsabs.harvard.edu/abs/2012EGUGA..14.9705O)<br>APOLIS LONG (Gridded data set, 100x100m, 1981-2016)<br>SPARTACUS v2.1 (Gridded data set, 1x1km, daily sunshine duration, 1961-2020).</li> <li><strong>Wind</strong>: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li><strong>Humidity</strong>: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li><strong>Digital elevation model</strong>: © Kooperation Länder, Bund (BEV, BML), 2022</li> </ul> <p> </p>
Indicators to evaluate ecosystem services of urban rivers.
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STUDYING INDICATORS OF THE ANTIOXIDANT SYSTEM FOR HASHIMOTO'S DISEASE IN ADOLESCENTS
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https://doi.org/10.1371/journal.pone.0282247.g008 in New species of Kuqaia from the Lower Jurassic of Sweden indicates a possible water flea (Crustacea: Branchiopoda) affinity
https://doi.org/10.1371/journal.pone.0282247.g008
Comparative analysis of Dipodomys species indicates that kangaroo rat hindlimb anatomy is adapted for rapid evasive leaping
<p><span>Body size is a key factor that influences antipredator behavior. For animals that rely on jumping to escape from predators, there is a theoretical trade-off between jump distance and acceleration as body size changes at both the inter- and intraspecific levels. Assuming geometric similarity, acceleration will decrease with increasing body size due to a smaller increase in muscle cross-sectional area than body mass. Smaller animals will likely have a similar jump distance as larger animals due to their shorter limbs and faster accelerations. Therefore, in order to maintain acceleration in a jump across different body sizes, hind limbs must be disproportionately bigger for larger animals. We explored this prediction using four species of kangaroo rats (<i>Dipodomys </i>spp.), a genus of bipedal rodent with similar morphology across a range of body sizes (40–150 g). Kangaroo rat jump performance was measured by simulating snake strikes to free-ranging individuals. Additionally, morphological measurements of hind limb muscles and segment lengths were obtained from thawed frozen specimens. Overall, jump acceleration was constant across body sizes and jump distance increased with increasing size. Additionally, kangaroo rat hind limb muscle mass and cross-sectional area scaled with positive allometry. Ankle extensor tendon cross-sectional area also scaled with positive allometry. Hind limb segment length scaled isometrically, with the exception of the metatarsals, which scaled with negative allometry. Overall, these findings support the hypothesis that kangaroo rat hind limbs are built to maintain jump acceleration rather than jump distance. Selective pressure from single-strike predators, such as snakes and owls, likely drives this relationship.</span></p>
Figure 6 from: Huemer P, Schmid J (2021) Relict populations of Lyonetia ledi Wocke, 1859 (Lepidoptera, Lyonetiidae) from the Alps indicate postglacial host-plant shift to the famous Alpenrose (Rhododendron ferrugineum L.). Alpine Entomology 5: 101-106. https://doi.org/10.3897/alpento.5.76930
Figure 6 Final instar larva of Lyonetia ledi on Rhododendron ferrugineum (Switzerland, Graubünden, Ardez).
Figures 2- 3 from: Huemer P, Schmid J (2021) Relict populations of Lyonetia ledi Wocke, 1859 (Lepidoptera, Lyonetiidae) from the Alps indicate postglacial host-plant shift to the famous Alpenrose (Rhododendron ferrugineum L.). Alpine Entomology 5: 101-106. https://doi.org/10.3897/alpento.5.76930
Figures 2- 3 Lyonetia ledi adults resting on leaves of Rhododendron ferrugineum (Switzerland, Graubünden, Ardez).
Figure 1 from: Huemer P, Schmid J (2021) Relict populations of Lyonetia ledi Wocke, 1859 (Lepidoptera, Lyonetiidae) from the Alps indicate postglacial host-plant shift to the famous Alpenrose (Rhododendron ferrugineum L.). Alpine Entomology 5: 101-106. https://doi.org/10.3897/alpento.5.76930
Figure 1 COI Neighbor-Joining tree of species in the studied Lyonetia. Note: the scale bar only applies to internal branches between species. Width of triangles represent sample size, depth the genetic variation within the cluster.
Figures 4- 5 from: Huemer P, Schmid J (2021) Relict populations of Lyonetia ledi Wocke, 1859 (Lepidoptera, Lyonetiidae) from the Alps indicate postglacial host-plant shift to the famous Alpenrose (Rhododendron ferrugineum L.). Alpine Entomology 5: 101-106. https://doi.org/10.3897/alpento.5.76930
Figures 4- 5 Leaf-mines of Lyonetia ledi on Rhododendron ferrugineum (Switzerland, Graubünden, Ardez).
Figures 7- 8 from: Huemer P, Schmid J (2021) Relict populations of Lyonetia ledi Wocke, 1859 (Lepidoptera, Lyonetiidae) from the Alps indicate postglacial host-plant shift to the famous Alpenrose (Rhododendron ferrugineum L.). Alpine Entomology 5: 101-106. https://doi.org/10.3897/alpento.5.76930
Figures 7- 8 Characteristic cocoon with final instar larva and pupa of Lyonetia ledi on Rhododendron ferrugineum (Switzerland, Graubünden, Ardez).
Figure 1 from: Bak PG, Belenichev IF, Kucherenko LI, Abramov AV, Khromylоva OV (2021) Morpho-functional indicators changes of rats' myocardium in experimental doxorubicin-induced chronic heart failure and its pharmacological modulation with new 4-amino-1,2,4-triazole derivative. Pharmacia 68(4): 919-925. https://doi.org/10.3897/pharmacia.68.e75298
Figure 1 Hypothetical mechanism of the compound "Hypertril" positive effect on the morpho-functional parameters of rat cardiomyocytes in experimental doxorubicin-induced CHF.
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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.