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3,206 results for “property (T)”
Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties
<p>This dataset contains the data from the publication</p> <p><strong>"Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"</strong></p> <p> </p> <p> </p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
Spatial models of topsoil properties in Romania using digital soil mapping techniques
<p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:</p> <p>Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, <em>Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning</em><strong>, PLOS ONE</strong>, 2023</p> <p>The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).</p> <p>The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).</p> <p>The soil variables are classified as follows:</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Classes</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>2</strong></p> </td> <td> <p><strong>3</strong></p> </td> <td> <p><strong>4</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><em>pH</em></p> </td> <td> <p>≤ 5</p> <p>(strongly acid)</p> </td> <td> <p>5.1 – 5.8 (moderately acid)</p> </td> <td> <p>5.9 – 6.8</p> <p>(weakly acid)</p> </td> <td> <p>6.9 – 7.2</p> <p>(neutral)</p> </td> <td> <p>7.3 – 8.4</p> <p>(weakly alkaline)</p> </td> <td> <p>8.5 – 8.8 (moderately alkaline)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>EC (mS m<sup>-1</sup>)</em></p> </td> <td> <p>≤ 12.75</p> </td> <td> <p>12.76 – 16.49</p> </td> <td> <p>16.50 – 20.04</p> </td> <td> <p>20.05 – 24.18</p> </td> <td> <p>24.19 – 29.11</p> </td> <td> <p>29.12 – 35.23</p> </td> <td> <p>≤ 35.24</p> </td> </tr> <tr> <td> <p><em>OC (g kg<sup>-1</sup>)</em></p> </td> <td> <p>< 7.5</p> <p>(very low)</p> </td> <td> <p>7.5 – 17.4</p> <p>(low)</p> </td> <td> <p>17.4 – 37.8 (moderate)</p> </td> <td> <p>37.8 – 61.0</p> <p>(high)</p> </td> <td> <p>> 61</p> <p>(very high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>CaCO<sub>3</sub></em></p> <p><em>(g kg<sup>-1</sup>)</em></p> </td> <td> <p>0</p> <p>(no carbonates)</p> </td> <td> <p>1 – 10</p> <p>(low)</p> </td> <td> <p>11 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 80</p> <p>(medium 2)</p> </td> <td> <p>81 – 107</p> <p>(medium 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>P (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>< 4</p> <p>(extremely low)</p> </td> <td> <p>4 – 8</p> <p>(very low)</p> </td> <td> <p>8 – 18</p> <p>(low)</p> </td> <td> <p>18 – 36</p> <p>(medium)</p> </td> <td> <p>36 – 72</p> <p>(high)</p> </td> <td> <p>> 72</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>N (g kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 1</p> <p>(very low)</p> </td> <td> <p>1.1 – 1.4</p> <p>(low)</p> </td> <td> <p>1.5 – 2.0</p> <p>(medium 1)</p> </td> <td> <p>2.1 – 2.7</p> <p>(medium 2)</p> </td> <td> <p>2.8 – 6.0</p> <p>(high)</p> </td> <td> <p>> 6</p> <p>(very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>K (mg kg<sup>-1</sup>)</em></p> </td> <td> <p>≤ 40 *</p> <p>(extremely low)</p> </td> <td> <p>41 – 65 *</p> <p>(very low)</p> </td> <td> <p>66 – 130</p> <p>(low)</p> </td> <td> <p>131 – 200 (medium)</p> </td> <td> <p>201 – 300</p> <p>(high)</p> </td> <td> <p>> 300</p> <p> (very high)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Clay (%)</em></p> </td> <td> <p>≤ 25</p> <p> (low 1)</p> </td> <td> <p>26 – 32</p> <p>(low 2)</p> </td> <td> <p>33 – 40</p> <p>(medium 1)</p> </td> <td> <p>41 – 45</p> <p>(medium 2)</p> </td> <td> <p>≥ 46</p> <p> (high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Silt (%)</em></p> </td> <td> <p>< 25</p> <p>(medium 1)</p> </td> <td> <p>25 – 32</p> <p>(medium 2)</p> </td> <td> <p>33 – 40</p> <p>(high 1)</p> </td> <td> <p>41 – 50</p> <p>(high 2)</p> </td> <td> <p>> 50</p> <p>(high 3)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>Sand (%)</em></p> </td> <td> <p>< 15</p> <p>(low 1)</p> </td> <td> <p>15 – 25</p> <p>(low 2)</p> </td> <td> <p>26 – 35</p> <p>(low 3)</p> </td> <td> <p>36 – 56</p> <p>(medium)</p> </td> <td> <p>> 56</p> <p>(high)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>* classes not present on the Romanian territory</p> <p> </p> <p> </p> <p> </p>
The influence of the properties of inorganic solvents on the hydrodynamic diameter of TiO2 nanoparticles
<p>In this model the property of a nanomaterial is predicted not on the basis of descriptors characterizing the chemical composition of nanoparticles or physical properties of the initial nanoforms, but on the basis of descriptors describing the dispersion medium (pH, IP, D3_HeteroNonMetals) and the property of nanoparticles dependent on it (Potential ζ). </p> <p>The observed small size of the hydrodynamic diameter of TiO2 in solvents of strong acids and bases compared to other solvents may indicate stronger repulsive interactions between nanoparticles than in the case of other systems. Moreover, in the case of salt solutions, the observed large size of the hydrodynamic diameter of TiO2 may be the result of a thicker electrical layer surrounding the particles in the dispersion system.</p>
Curated dataset on protein's properties and post-translational modification protein properties
<p>Proteins perform essential cellular functions, which range from cell division and metabolism to DNA replication. Thus, decoding the mechanism of action of cells, requires understanding of the functioning and physicochemical properties of proteins [1]. While the genetic code encodes the primary structure of proteins, they undergo various modifications as part of their normal functioning including addition of modifying groups, such as acetyl, phosphoryl, glycosyl, and methyl, to one or more amino acids after translation, which is known as post-translational modification (PTM) [2, 3]. PTMs play an essential role in regulating protein functions by altering their physicochemical properties and understanding these reactions provides valuable insights regarding cell function. Advances in proteomics research have significantly deepened our understanding of PTMs and their impact on cellular functions and disease mechanisms. The study of PTMs is now at the forefront of research in molecular biology and biochemistry.</p> <p>Many databases, software, and tools have been developed to enhance our understanding of the various PTMs that affect human plasma proteins and help to simplify the analysis of complex PTM data [4]. These PTM databases and tools contain significant information and are a valuable resource for the research community. Key databases include dbPTM, UniProt, and PubChem. Utilising these databases, protein-related information like substrate peptides, amino acid sequence numbers, and experimentally validated PTM sites can be identified and curated.</p> <p>This dataset presents curated information regarding PTM-related changes in the physicochemical properties of the 16 most abundant plasma proteins [5], i.e., Serum Albumin, Serotransferrin, Antithrombin-III, Apolipoprotein A-I, Apolipoprotein A-IV, Apolipoprotein B-100, Apolipoprotein C-II, Apolipoprotein C-III, Apolipoprotein E, Clusterin, Complement C3, Haptoglobin, Histidine-rich glycoprotein, Mannose-binding protein C, Hemoglobin, and Fibrinogen alpha chain. The physicochemical properties studied, and the impact of different PTMs on the properties, include the protein molecular weight, isoelectric point, surface hydrophobicity, and solubility. The PTMs explored include phosphorylation, acetylation, glycosylation, methylation, ubiquitination, SUMOylation, lipidation, glutathionylation, nitrosylation, sulfoxidation, succinylation, neddylation, malonylation, hydroxylation, oxidation, and palmitoylation.</p> <p>References</p> <ol> <li>Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 4th edition. New York: Garland Science; 2002. Analyzing Protein Structure and Function.</li> <li>Chen, H.; Venkat, S.; McGuire, P.; Gan, Q.; Fan, C. Recent Development of Genetic Code Expansion for Posttranslational Modification Studies. Molecules 2018, 23, 1662.</li> <li>Marc Oeller, Ryan Kang, Hannah Bolt, Ana Gomes dos Santos, Annika Langborg Weinmann, Antonios Nikitidis, Pavol Zlatoidsky, Wu Su, Werngard Czechtizky, Leonardo De Maria,Pietro Sormanni, Michele Vendruscolo: Sequence-based prediction of the solubility of peptides containing non-natural amino acids [bioRiv].</li> <li>Ramazi S, Zahiri J. Posttranslational modifications in proteins: resources, tools and prediction methods. Database (Oxford). 2021 Apr 7;2021:baab012.</li> </ol>
Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid
<p>This dataset contains measurement data from the following publication: Belt, T.; Kyyrö, S.; Kilpinen, A. T. (2023) Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid. Journal of Materials Science, 10.1007/s10853-023-08874-w. Small samples of Scots pine sapwood were modified using different concentrations of phenol formaldehyde (2.5, 5, 10, 20 and 30% resin solids content) and sorbitol-citric acid (5, 10, 20, 30 and 40% resin solids content) and then exposed to brown rot decay by <em>Coniophora puteana</em> and <em>Rhodonia placenta</em>. Sample masses and dimensions were measured at different points to determine their weight gain, anti-swelling efficiency and moisture exclusion efficiency due to modification, their mass loss due to decay and their moisture content at the end of the decay test. Fluorescence images were collected from decayed and control samples after the decay test. Further details on the experimental procedures can be found in the publication. </p> <p>The "Sample IDs and measurement data.csv" -file contains the sample IDs and all measured dimensions and mass data for every sample. Areas A<sub>dry0</sub>, Ad<sub>ry1</sub>, A<sub>wet</sub>, and A<sub>dry2</sub> are the cross-sectional areas of the samples in the dry state before modification, in the dry state after modification and before leaching, in the wet state during leaching, and in the dry state after leaching, respectively. Masses m<sub>dry0</sub>, m<sub>dry1</sub>, m<sub>dry2</sub>, m<sub>RH85</sub>, m<sub>wet</sub>, and m<sub>dry3</sub> are the masses of the samples in the dry state before modification, in the dry state after modification and before leaching, in the dry state after leaching, in the conditioned state at RH 85%, in the wet state at the end of the decay test, and in the dry state after the decay test, respectively.</p> <p>The "Fluorescence images" -folder contains fluorescence images collected from the samples. The image files are named according to the ID of the imaged sample, followed by additional tags. The samples modified using phenol formaldehyde were imaged using both green and UV excitation, and the file names contain the tag "green" or "UV" to denote the used excitation wavelengths. For all samples, the sample ID (and the excitation tag) are followed by a number to differentiate replicate images collected from the sample. </p>
Phantom measurement data for 'Configuration-based electrical properties tomography', Iyyakkunnel et al. (2021)
<p>This dataset contains the phantom bSSFP measurement data used in the published article Iyyakkunnel et al., 'Configuration-based electrical properties tomography', Magn Reson Med. 2021;85:1855–1864 (doi: 10.1002/mrm.28542). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br> The data includes the magnitude and phase measurements for eight phase-cycled scans (in dicom (.dcm) format). The RF phase increment for the phase cycled scans corresponds to 0°, 45°, 90°, 135°, 180°, 225°, 270° and 315°. For further measurement details, please refer to the mentioned original article.</p>
Phantom measurement data for 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Iyyakkunnel et al. (2022)
<p>This dataset contains the phantom measurement data used in the published article Iyyakkunnel et al., 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Magn Reson Med. 2022;87:1250–1260 (doi: 10.1002/mrm.29020). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using the body coil for transmission and the a 20-channel head and neck coil for reception. Phase images from multichannel coil data were reconstructed using the manufacturer’s “adaptive coil combine” method. Magnitude measurements for B1 reconstruction using actucal flip angle imaging (AFI) is also included. Phantom scans were also performed with a 1H/23Na transmit/receive birdcage coil (RAPID Biomedical, Rimpar, Germany) for the Supporting Information Figure S1. The data includes the magnitude and phase measurements for the suggested Carr Purcell spin echo sequence with 10 echoes (in dicom (.dcm) format). For further measurement details, please refer to the mentioned original article.</p>
Psychometric Properties of the Maslach Burnout Inventory in Healthcare Professionals, Ancash Region, Peru
<p><strong>Background:</strong> Burnout syndrome (BS) among healthcare professionals in Peru demands immediate attention. Consequently, there is a need for a validated and standardized instrument to measure and address it effectively. This study aimed to determine the psychometric properties of the Maslach Burnout Inventory (MBI) among healthcare professionals in the Ancash region of Peru.</p> <p><strong>Methods:</strong> Using an instrumental design, this study included 303 subjects of both sexes (77.56% women), ranging in age from 22 to 68 years (M = 44.46, SD = 12.25), selected via purposive non-probability sampling. Appropriate content validity, internal structure validity, and item internal consistency were achieved through confirmatory factor analysis, and discriminant validity for the three dimensions was obtained. Evidence of convergent validity was found for the Emotional Exhaustion (EE) and Personal Accomplishment (PA) dimensions, with reliability values (ω > .75).</p> <p><strong>Results:</strong> The EE and PA dimensions exhibited acceptable levels of reliability (ω and α > .80). However, the Depersonalization (DP) dimension demonstrated significantly lower reliability (α < .60 and ω < .50).</p> <p><strong>Conclusions:</strong> A correlated three-factor model was confirmed, with most items presenting satisfactory factor loadings and inter-item correlations. Nonetheless, convergent validity was not confirmed for the DP dimension.</p> <p><strong>Keywords: </strong>Burnout; Psychometrics; Validity; Reliability; Healthcare Professionals.</p>
Boron-, carbon-, and silicon-bridged 1,12-dihydroxy-perylene bisimides with tuned structural and optical properties
<p>Additional data to report <a href="https://doi.org/10.1039/D3CC03704E">https://doi.org/10.1039/D3QO01389H</a></p> <p>Establishing suitable design strategies to tailor the functional properties of perylene bisimide (PBI) dyes are critical for their successful application in various devices. Herein, we report a new synthetic strategy to tune their structural and fluorescence properties by employing 1,12-bay-substitution pattern that has been seldomly investigated in the past. Central to the strategy is the use of 1,12-dihydroxy-PBI as a starting compound and the subsequent bridging of these hydroxy bay-functional groups with either a boron, carbon or silicon atom resulting in derivatives with rigidified perylene core. This is followed by a detailed exploration of synthetic possibilities to functionalize the unsubstituted 6,7-positions at the opposite bay area to achieve novel perylene dyes with excellent structural and optical properties. The fluorescence color could be tuned from green to dark-orange while retaining the almost unity fluorescence quantum yield in solution. Moreover, a strong fluorescence with quantum yields as high as 40% has been observed for powders, which clearly illustrates the potential of the presented structural design to obtain new solid-state emitters.</p>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
UCSB SONGS Mitigation Monitoring: Wetland Process Study – Soil Properties
These data describe physical and chemical properties of soil samples collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2019 at the San Dieguito Wetland in San Diego County, CA. Additional locations at Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh is Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA were added in 2021. Sampling occurred sporadically at various locations in each wetland. All soil samples were characterized for organic matter content and particle size. Additional properties were characterized for select soil samples.
The relationship between early succession rates and soil properties in the Andrews Experimental Forest, 1999-2000
This study represents only one portion of a much larger study involving a wide range of disciplines and several H.J. Andrews Forest researchers. This dataset represents all the soils data collected up through the summer of 1999. In addition, Steve Acker conducted surveys of vegetation and measuring tree growth using cores. Mark Harmon has conducted a survey of coarse woody debris. Future studies could include hydrology and species diversity. When we compared the soil characteristics between slow and expected recovery sites, the only variables showing significant differences were soil moisture, litter depth and substrate induced respiration (SIR) rates at low glucose concentrations. The litter depth was slightly less, soil moisture lower, and SIR rates higher in the slow sites. When we compared soils in adjacent uncut forests, we found that field respiration rates were lower in forests adjacent to slow recovery sites than to normal sites suggesting that these sites may have inherently lower productivities. We concluded that slow recovery after clear-cutting is most likely related to physical site characteristics; i.e. steepness of slope and aspect. There did not appear to be any difference in soil depth.
Chemical and microbiological properties of soils in the Andrews Experimental Forest (1994 REU Study)
To conduct a comprehensive study of soil chemical and microbiological properties at the HJA during the week of July 11,1994.
Soil properties and nutrient concentrations by depth from the Anaktuvuk River Fire site in 2011
Below ground soil bulk density, carbon and nitrogen was measured at various depth increments in mineral and organic soil layers at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. This data corresponds with the aboveground biomass and root biomass data files: 2011ARF_AbvgroundBiomassCN, 2011ARF_RootBiomassCN_byDepth, 2011ARF_RootBiomassCN_byQuad, 2011ARF_RootBiomassCN_byQuad.
Soil physical and chemical properties based on genetic horizon from 4 replicate pits placed around the replicate LTER control plots sampled in 1988 and 1989.
Dataset contains the following soil properties for each genetic horizon - site, Soil pit, upper and lower boundary (cm), Mg meq/100gm, Ca meq/100gm, K meq/100gm, CEC meq/100gm, pH, %C, %sand, %silt, %clay, Total %N, Total %P, % organic matter, Mn meq/100gm, Available-P ppm, %CO3, bulk density gm/cm3, Volume wt gm/m2.
Patterns of and controls over nitrogen inputs by green alder (Alnus viridis spp. fruticosa) to a secondary successional chronosequence in interior Alaska II - Soil Physical and Chemical Properties
In September of 1999 we collected soil cores to identify stage, replicate stand, canopy, and soil horizon patterns of soil physical (color, bulk density, pH) and chemical (N, C, P) parameters.
February- March 2010 RTK survey of salt marsh plant ground elevations, plant characteristics and soil properties to support analysis of a LIDAR-derived DEM.
Real time kinematic (RTK) GPS survey of ground elevations for six plant species (Spartina alterniflora, Juncus roemerianus, Batis maritima, Distichlis spicata, Salicornia virginica and Borrichia frutescens) and two non-vegetated cover classes (salt pan and intertidal mud) was carried out from February to March 2010 to assess the accuracy of a LIDAR-derived digital elevation model. In total, 369 ground control points (GCP) were collected for the Duplin River (Sapelo Island) and Blackbeard Creek (Blackbeard Island) salt marshes with associated plant and soil charactistics (soil salinity and water content, soil organic matter, soil redox potential). Data were collected to examine the relationships between marsh soil elevation, plant habitat distribution and soil properties and to support the analysis of a a LIDAR-derived digital elevation model (DEM) and RTK data collect in 2009.
Hubbard Brook Experimental Forest: Soil Acid-Base Properties and Microbial Activity, Watershed 1 and West of Watershed 6 (2015-2016)
In summer 2015 and spring 2016, researchers collected soils from the CaSiO3-enriched watershed at Hubbard Brook (W1) and from a nearby site west of the reference watershed at Hubbard Brook (W6). These soils were sampled throughout the hardwood zone of each watershed, and the sampling scheme explicitly examined pit-and-mound microtopographic gradients. These soils were analyzed for acid-base properties, net and gross N cycling rates, microbial biomass, and C cycling rates. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
PSC01 Exploring effects on stream ecosystem properties by two size classes of prairie stream cyprinids
Losses in freshwater fish diversity might produce a loss in important ecological services provided by fishes in particular habitats. An important gap in our understanding of ecosystem services by fishes is the influence of individuals from different size classes, which is predicted based on known ontogenetic shifts in habitat and diet. I used twenty experimental stream mesocosms located on Konza Prairie Biological Station (KPBS), KS, USA to assess the influence of fish size on ecosystem properties. Mesocosms included two macrohabitats: one riffle upstream from one pool filled with consistent pebble and gravel substrate. There were four experimental and one control treatment, each replicated four times (N = 20). I used two size classes of Central Stonerollers (Campostoma anomalum) and Southern Redbelly Dace (Chrosomus erythrogaster). Five ecosystem properties were assessed: algal filament length (cm), benthic chlorophyll a (µg/cm2), benthic organic matter (g/m2), macroinvertebrate biomass (g/m2), and stream metabolism (g O2/m2/day1). Size structure of fish populations affected some, but not all, ecosystem properties and these effects were dependent upon species identity. Size structure of both species had effects on algal filament lengths where stonerollers of both size classes reduced algal filaments, but only small redbelly dace kept filaments short. A better understanding of the relationship between these prairie stream minnows and their small stream habitats could be useful to both predict changes in stream properties if species are lost or size structure shifts, and to redbelly dace populations, a Species In Need of Conservation.
ScienceDex guides
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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.