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958 results for “Data quality”
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Data related to Mapping user-related comfort conditions to identify urban planning issues for improved quality of places in cities: The case of Ljubljana, Slovenia
<p>The data consists of parameters including air temperature, noise levels, humidity, and air quality (PM 2.5), measured using a portable ICT device while cycling through the city of Ljubljana between August 1 and August 30, 2022. The data is available as raw CSV files, with each measurement session stored in a separate file. An additional QGIS Project file (qgz) is included, along with maps (in jpg format in ppt) that visually represent the results presented in the article.</p>
Daily water quality data sets of the nine U.S. watersheds
<p>US_daily_WQ_datasets_2.xlsx contains daily water quality (WQ) and discharge data from the nine watersheds in the United States. The data value of -999 means missing observation. The names (USGS station numbers) of nine WQ monitoring sites are Blanchard River (04189000), Cuyahoga River (0428000), Great Miami River (03271500), Honey Creek (04197100), Maumee River (04193500), Muskingum River (03150000), Portage River (04195500), Rock Creek (04197170), and Tiffin River (04185000). Daily WQ data were composed by following the procedure described in the Appendix S1 in Hirsch (2014). The all WQ data river were retrieved from the Heidelberg University's National Center for Water Quality Research site (https://ncwqr.org/monitoring/data) and discharge data were downloaded from the USGS National Water Information System (<a href="http://waterdata.usgs.gov/nwis/">http://waterdata.usgs.gov/nwis/</a> or https://doi.org/10.5066/F7P55KJN) in 2020. All the daily discharge data were acquired via the USGS National Water Information System.</p> <p>These data were used to evaluate the performance of the unbiased load estimates and confidence intervals of river loads based on the rating curve method using rejection sampling in the listed article below. To maintain the traceability of the proposed load estimation method and replicability of the results in the article, the authors of the article upload the data used in this repository.</p>
List of known SNP positions (based on SNP chip data) for base quality score recalibration of alignments for whole-genome resequencing and whole-genome bisulfite sequencing data from great tits (Parus major)
<p>The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulfite sequencing is commonly used for assessing genome-wide DNA methylation at single nucleotide resolution but these data can also provide information on genetic variants like single nucleotide polymorphisms (SNPs). However, bisulfite conversion causes unmethylated cytosines to appear as thymines, complicating the alignment and subsequent SNP calling. Several tools have been developed to overcome this challenge, but there is no independent evaluation of such tools for non-model species, which often lack genomic references. Here, we used whole-genome bisulfite sequencing (WGBS) data from four female great tits (<i>Parus major</i>) to evaluate the performance of seven tools for SNP calling from bisulfite sequencing data. We used SNPs from whole-genome resequencing data of the same samples as baseline SNPs to assess common performance metrics like sensitivity, precision, and the number of true positive, false positive, and false negative SNPs for the full range of variant and genotype quality values. We found clear differences between the tools in either optimizing precision (Bis-SNP), sensitivity (biscuit), or a compromise between both (all other tools). Overall, the choice of SNP caller strongly depends on which performance parameter should be maximized and whether ascertainment bias should be minimized to optimize downstream analysis, highlighting the need for studies that assess such differences.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Data Release for "The curious case of GW200129: interplay between spin-precession inference and data-quality issues"
<p>Data Release associated with <a href="https://arxiv.org/abs/2206.11932">The curious case of GW200129: interplay between spin-precession inference and data-quality issues Data Release</a>. We release frame files and result file for selected parameter estimation runs in the paper.</p> <p> </p> <p>Each directory contains .json bilby result files for the PE run described by that directory. The specific channel names and frame files that we used in the PE runs are listed below. The frames for the PE runs with BayesWave glitch subtraction are included in this release and are in BW_frames/frame_{glitch label}.</p> <p> </p> <p>L1 data with glitch subtraction:</p> <p>Channel: L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4</p> <p>Link: https://zenodo.org/record/5546680/files/L-L1_HOFT_CLEAN_SUB60HZ_C01_P1800169_v4-1264314068-4096.gwf</p> <p> </p> <p>L1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (L1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080/L-L1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>H1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (H1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080HL-H1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>Channels for runs with BayesWave glitch-subtracted frames--</p> <p>(Note you need to read in the correct gwf file to access each channel; for example the channel for BayesWave glitch A should be accessed after reading in the gwf file in `BW_frames/frame_A/`)</p> <p>BayesWave glitch A (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_30000</p> <p>BayesWave glitch B (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_28395</p> <p>BayesWave glitch C (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_32752</p>
Vesuvio cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Campi Flegrei cGPS network – RINEX data quality control (2000 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Ischia cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Supplemental data for: Longitudinal, multi-platform metagenomics yields a high-quality genomic catalog and guides an in vitro model for cheese communities
<p><span>Microbiomes are intricately intertwined with human health, geochemical cycles, and food production. While many microbiomes of interest are highly complex and experimentally intractable, cheese rind microbiomes have proven powerful model systems for the study of microbial interactions. To provide a more comprehensive view of the genomic potential and temporal dynamics of cheese rind communities, we combine longitudinal, multi-platform metagenomics of three ripening washed-rind cheeses with whole genome sequencing of community isolates. Sequencing-based approaches revealed a highly reproducible microbial succession in each cheese, co-existence of closely related <em>Psychrobacter</em> species, and enabled the prediction of plasmid and phage diversity and their host associations. Combined with culture-based approaches, we established a genomic catalog and a paired 16-member in vitro washed rind cheese system. The combination of multi-platform metagenomic time-series data and an <em>in vitro</em> model provides a rich resource for further investigation of cheese rind microbiomes both computationally and experimentally. </span></p>
Air quality data from the article "Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment"
<p>This dataset consists of 26 files. The descriptions of the files are as follows:</p> <ul> <li>aqi_TY.csv, pm25_TY.csv, pm10_TY.csv, so2_TY.csv, no2_TY.csv and o3_TY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY.csv, pm25_NTY.csv, pm10_NTY.csv, so2_NTY.csv, no2_NTY.csv and o3_NTY.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 36 monitoring stations used in model establish stage on NTY days. The time range is June 2014 to December 2020.</li> <li>station_info.csv is the detailed information of the 36 monitoring stations used in model establish stage, including station number, city, longitude and latitude.</li> <li>aqi_TY_testing.csv, pm25_TY_testing.csv, pm10_TY_testing.csv, so2_TY_testing.csv, no2_TY_testing.csv and o3_TY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY_testing.csv, pm25_NTY_testing.csv, pm10_NTY_testing.csv, so2_NTY_testing.csv, no2_NTY_testing.csv and o3_NTY_testing.csv are the observed values of AQI and concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> of 3 monitoring stations used for testing the model on NTY days. The time range is June 2014 to December 2020.</li> <li>sta_testing.csv is the detailed information of the 3 monitoring stations used for testing the model, including station number, city, longitude and latitude.</li> </ul>
Data from: Validation of Quality-of-Life assessment tool for Ethiopian old age people
<p><strong>Background</strong>: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Ethiopia.</p> <p><strong>Methods</strong>: A cross-sectional study was conducted among 180 community-dwelling old age people in Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach's alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.</p> <p><strong>Results</strong>: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population, and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach's Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis's index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.</p> <p><strong>Conclusion</strong>: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.</p>
Data from: Phosphorus limitation determines the quality of dissolved organic matter released by marine heterotrophic prokaryotes
<p>We determined phosphorus (P) limitation effect on the quantity and quality of dissolved organic matter (DOM) released by heterotrophic prokaryotes (HP). We grew 2 single bacterial strains, Photobacterium angustum and Sphingopyxis alaskensis, and natural HP communities collected in fall and spring from the Mediterranean Sea, on glucose under 2 treatments: P-replete vs. P-limiting. DOM release by HP comprised up to 30 % of the initial carbon provided for growth. P availability influenced carbon allocation to different cellular processes (respiration vs. growth), but did not significantly affect the quantity of DOM released by HP. However, using fluorescence spectroscopy, we demonstrated an effect of P-limitation on DOM quality, with a predominance of humic-like compounds under P-limitation but protein-like compounds under P-repletion. Our results suggest that P-limitation could determine the fate of HP-derived DOM in the ocean, thus affecting the microbial carbon pump.</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Data from: Cryptic male mate choice for high-quality females reduces male postcopulatory success in future matings
<p>Cryptic male mate choice occurs when males differentially allocate resources to females during or after copulation. When male resources are limited, males may benefit by strategically allocating more resources toward higher-quality females. In the fruit fly, <em>Drosophila melanogaster</em>, males mate for longer and may transfer more sperm and more seminal proteins when they mate with larger females compared to smaller females. It is unclear, however, whether this increased investment in large females has any impact on the males' later matings. We mated <em>D. melanogaster</em> males sequentially to females of large or small body size in all possible combinations to test whether cryptic male mate choice for large females is costly to the males' subsequent matings. Second matings were shorter for males compared to their first matings, but there were no differences in fecundity between females mated first or second by a male. Interestingly, male success at defensive sperm competition declined between his first and second matings only when his first mating had been with a large female. This suggests that the higher initial investment in large females reduced male postcopulatory success in their subsequent matings. Cryptic male mate choice may carry underappreciated costs to males that could limit their reproductive potential.</p>
qechairquality: Air Quality Data at Queen Elizabeth Central Hospital (QECH), Blantyre, Malawi.
<p>Air quality data with measurements in 5-minute intervals for particulate matter (PM 2.5 & PM 10) at eight sensor locations over two months at Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi.</p>
Stormwater filter water quality and quantity data - treatment of road runoff in the city of Vantaa, Finland
<p>The water quantity and quality dataset is from road runoff filters in the city of Vantaa, Finland. Data are from 6 campaigns in 2017 and 2019. The data is documented in: Koivusalo, H., Dubovik, M., Wendling, L., Assmuth, E., Sillanpää, N., Kokkonen, T. 2023. Performance of sand and mixed sand-biochar filters for treatment of road runoff quantity and quality. Accepted to Water. </p> <p> </p>
Data from: Effects of light quality on colonization of tomato roots by arbuscular mycorrhizal fungus (AMF) and implications for growth and defense
<p>Beneficial soil microbes can enhance plant growth and defense, but the extent to which this occurs depends on the availability of resources, such as water and nutrients. However, relatively little is known about the role of light quality, which is altered during shading, resulting in a low red: far-red ratio (R:FR) of light. We examined how low R:FR light influences arbuscular mycorrhizal fungus (AMF)-mediated changes in plant growth and defense using <em>Solanum lycopersicum </em>(tomato) and the insect herbivore <em>Chrysodeixis chalcites</em>. We also examined effects on third trophic level interactions with the parasitoid <em>Cotesia marginiventris</em>. Under low R:FR light, non-mycorrhizal plants activated the shade avoidance syndrome (SAS), resulting in enhanced biomass production. However, mycorrhizal inoculation decreased stem elongation in shaded plants, thus counteracting the plant's SAS response to shading. Unexpectedly, activation of SAS under low R:FR light did not increase plant susceptibility to the herbivore in either non-mycorrhizal or mycorrhizal plants. AMF did not significantly affect survival or growth of caterpillars and parasitoids but suppressed herbivore-induced expression of jasmonic acid-signaled defenses genes under low R:FR light. These results highlight the context-dependency of AMF effects on plant growth and defense and the potentially adverse effects of AMF under shading.</p>
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.