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13,113 results for “Resistivity”
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Data from: mPRIME Study - Interaction of Insulin Resistance with Cognition, Lifestyle, and Mental Health
<p>The presented datasets were collected within the <em>m</em>PRIME study, a prospective, observational study of the H2020 project Prevention and Remediation of Insulin Multimorbidity in Europe (PRIME) (grant No. 847879). The study investigates the interaction of insulin resistance with cognition, lifestyle, and mental health by combining traditional methods with ambulatory assessment and sensor-based data collection. Recruitment took place between March 2021 and March 2023 at the University Hospital Frankfurt, Germany.</p> <p>The eligibility criteria for the study were as follows: Age above 18 years, no intake of antidiabetic medication, insulin or glucocorticoids, no existing type 1 diabetes mellitus or gestational diabetes, no diagnoses of bipolar I disorder, schizophrenia, organically caused mental disorders and substance dependence, no severe neurological disorders, no current pregnancy or breastfeeding, no non-correctable visual impairments, no participation in medication-related studies within the last 6 months, no use of weight-reducing medications or a diet within the last 3 months, sufficient proficiency in German to complete questionnaires and neuropsychological tests.</p> <p>All participants in the <em>m</em>PRIME study provided written informed consent. The study protocol and procedures were approved by the local ethics committee.</p> <p><strong>Study Design</strong></p> <p>Individuals completed a baseline assessment and a one-week ambulatory assessment. The baseline assessment included: socio-demographic information, blood samples, anthropometric measures, neuropsychological tests, and several questionnaires. In addition, individuals were introduced to smartphone-based ecological momentary assessment (EMA), food protocols, and the use of sensors (continuous glucose monitor, accelerometer). Food protocols and EMA were conducted on three consecutive days, including two weekdays and one weekend day (Thursday to Saturday or Sunday to Tuesday). Several times a day, individuals were prompted via their smartphone to complete a working memory task and answer questions about stress, affect, and food intake. The continuous glucose monitor and accelerometer were worn continuously for 1 week.</p> <p> </p>
Effects of ON/OFF deep brain stimulation on cognitive control in treatment-resistant depression (EEG)
Open the record for dataset details and reuse information.
3D resistivity structure of the Los Humeros geothermal field.
<p>The dataset is the final three-dimensional resistivity model of the high temperature geothermal field Los Humeros, in Mexico.</p> <p>The model is described in deliverable 5.2 of the GEMex Project, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund<br> CONACYT-SENER, Project 2015-04-268074.</p>
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Dataset: Single nucleotide switches confer bacteriophage resistance to Pseudomonas protegens
<p>Dataset containing : <br>- csv files : Output file of the SNPs identified in all the phage-resistant variants (C2, C4, C17 and C18).</p> <p>- Excel files : </p> <ul> <li>Raw and pre-analyzed data for the bacterial growth analysis. (<a href="https://zenodo.org/api/records/15696172/draft/files/Bacterial_growth.xlsx/content" target="_blank" rel="noopener noreferrer">Bacterial_growth.xlsx</a>)</li> <li>Raw and pre-analised data for the competition assays (Compatition assays.xlsx). </li> <li>Raw and pre-analised data for the fitness assays in planta (plant experiment.xlsx) </li> <li><span lang="EN-US">Raw data of the phage adsorption assay (phage adsorption assays.xlsx) </span></li> </ul> <p>- Code used for the analysis of all the data.</p> <p>- Image and data pre analysed for the spatial distribution of the bacteria during competition in vitro (drop_competition.rar)</p> <ul> <li>Images of the colonies (GFP and red channel) in bmp format</li> <li>R code used to process and analyse this data (Phage_JV_drop.Rmd)</li> </ul> <p> </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Data for: Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy
<p><em><strong>Version v2:</strong> <br></em>Added tiff-image stacks</p> <p><em><strong>Version v1:</strong><br></em>The data is supplementary to the publication "Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy", DOI: <a href="https://doi.org/10.1016/j.compscitech.2022.109362">10.1016/j.compscitech.2022.109362</a> as well as to the dissertation: "Morphology and Fracture of Block Copolymer and Core-Shell Rubber Particle Modified Epoxies and their Carbon Fibre Reinforced Composites", urn: <a href="https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-63437">urn:nbn:de:hbz:386-kluedo-63437</a></p> <p>Key words: Polymer-matrix composites (PMCs), Impact behaviour, Low velocity impact, Barely visible impact damage, Damage tolerance, X-ray computed tomography, Fractography, Carbon fibre reinforced composite (CFRP)</p> <p>The data set is a collection of TXRM data of several low energy impact damages in CFRP specimens. The data was acquired via XCT (X-Ray Computed Tomography).</p> <p>Material details:</p> <ul> <li>Carbon fibre reinforced composite</li> <li>Thickness: ~ 1.65mm</li> <li>Matrix polymer: Epoxy-based (DGEBA): Sika CR144 + Anhydride curing agent (Huntsman Aradur917) + 1-Methylimidazole</li> <li>Carfon-fibre fabric: ECC Carbon fabric Style 763, based on Toho Tenax HTA40 E13, 140g/m²</li> <li>Layup: 13 layers, stacking sequence (45/-45/45/-45/90/0/90)s, (15% 0°/23% 90°/62% ± 45°) </li> <li>the average carbon fibre volume content was 52.5 ± 1.8 vol.-%</li> <li>cured ply-thickness: 126.1 μm</li> <li>Impact energies: 1J, 3J, 7J, 9J, 13J</li> <li>manufactured via autoclaving</li> </ul> <p>The reasearch received funding from the German Academic Exchange Service (DAAD) within the funding program “Kurzstipendien fuer Doktoranden” (grant number: 57438025).</p>
Dataset on the experimental investigation of the seismic response of moment-resisting steel frames using shaking table tests
<h2>Description:</h2> <p>This dataset contains data from experimental shake table tests conducted on a two-storey steel-frame structure, involving linear sweep, white noise, impulse, and seismic excitations (see 'Load_Protocols_v1.0.0.pdf'). The experiments were carried out using the uniaxial shaking table at the <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">RWTHDynLab</a> of the <a href="https://www.lbb.rwth-aachen.de/go/id/eaxh/">Chair of Structural Analysis and Dynamics (LBB) - RWTH Aachen University</a>, in cooperation with the <a href="https://www.stb.rwth-aachen.de/cms/~iozv/STB/">Institute of Structural Steel (STB) - RWTH Aachen</a> and the <a href="https://www.cwe.rwth-aachen.de/home-2/">Center for Wind and Earthquake Engineering (CWE) – RWTH Aachen</a>.</p> <p>The experimental campaign was developed to gain a better understanding of the interaction between the main structure and non-structural components, to investigate the reliability and accuracy of analytical methods to predict response floor spectra and non-structural component acceleration described in various guidelines and seismic codes. Regarding applications of Structural Health Monitoring the necessity for additional sensors on non-structural components was studied. Three single-degree-of-freedom oscillators (SDOFs) were connected to the upper floor representing non-structural components. The test structure was subjected to a total of twelve earthquake excitations with different spectral properties. The main objectives of the test campaign were:</p> <ul> <li>Identification of the modal properties of the test structure.</li> <li>Measurement of the floor response in terms of acceleration and displacement.</li> <li>Determination of the real floor response spectra based on the measurements of the acceleration sensors installed on the first and second floors.</li> <li>Comparison of the expected peak accelerations from the floor response spectra with the peak accelerations measured by the accelerometers attached to the three SDOFs.</li> </ul> <h3>Test structure:</h3> <p>The test structure consisted of a two-storey steel structure which was stabilised in the direction of excitation by moment resisting frames (MRFs). In the transverse direction the global stability was ensured by concentrically braced frames (braces QRo 50x5). The structure was designed in accordance with provisions of prEN-1998-1 for energy dissipation and ductile seismic behaviour. As ductile members were considered the frame beams so that the columns and connections remain undamaged. An illustration of the test structure is depicted in 'Test_Structure_Sketch_v1.0.0.pdf'. The dimensions of the test structure are: 2.40 m in length, 2.40 m in width and 3.78 m in total height (1st storey: 2.02 m; 2nd storey: 1.76 m). Four large steel I-sections, each with a dead weight of 1700 kg, were attached to the main structure as masses and secured by U-Profiles. A tank with a dead load of approx. 100 kg and a volume of 400 litres was mounted onto the first floor. The tank remained empty during this test series. HEA200 profiles (S355-J2) were selected as column profiles, whereas IPE160 profiles (S235-JR) as frame beams. All main and secondary beams were realised by HEA140 profiles (S235-JR). Four L60x6 bars were arranged in a rhombus shape in the floor plane to ensure a diaphragm action. In the area of the MRF connections, the columns were reinforced with an additional double web plate (t = 10 mm) and with three ribs (t = 10 mm) at the level of the beam top flange, the beam bottom flange and the haunch flange. The beam-to-column connections were classified as full strength and semi-rigid in terms of capacity and stiffness. The critical welds connecting the frame transom to the top plate (t = 15 mm) were designed as full penetration groove welds in accordance with the specifications of Annex E of prEN1998-1 for seismically standardised connections. Twelve M16-10.9 bolts were used to ensure the force transfer between the beam and column. All connections of the secondary beams to the main beams were realised as end plate connections to prevent premature failure due to combined loading by normal and shear forces. The arrangement of the secondary beams and the corresponding force transmission was conceptualized in such a way that the frame beams could be replaced after a series of tests without having to remove the masses and the tank. The columns were hinged to the shaking table (see 'Column_Base_Anchorage_v1.0.0.pdf'). Slots in the anchor plates allow the rotation of the support base around the strong axis of the columns. The anchoring to the shaking table was realised using four M24-8.8 threaded rods. To simulate non-structural components, three SDOFs were attached to the centre of the secondary beams that run across the frame transom on the second floor (see 'Test_Structure_v1.0.0.pdf'). The SDOFs consisted of a flat steel and a mass. Depending on the thickness of the flat steel and the position of the mass, the three SDOFs were calibrated so that the natural frequency of the first SDOF matches the natural frequency of the second modal shape of the test structure in the frame direction, and the natural frequency of the third SDOF corresponds the first natural frequency of the structure. The natural frequency of the second SDOF was set so that it lies between those of the other SDOFs, creating a staggered range of dynamic responses.</p> <h3>Test setup:</h3> <p>The shaking table specifications are:</p> <ul> <li>Table size: 3.0x3.0 m</li> <li>Max. specimen mass: 10 t</li> <li>Max. overturning moment: 30 m t</li> <li>Max. actuator stroke: +/- 250 mm</li> <li>Max. table velocity: +/- 1 m/s at rated load</li> <li>Max. table acceleration: +/- 1g at rated load</li> <li>Test frequency: 0 to 50 Hz</li> </ul> <p>The instrumentation scheme of the test setup consisted of accelerometers and displacement tranducers, measuring the excitation provided by the shaking table and the response of the structure. Regarding the global response of the test structure, the recordings of the accelerometers and displacement tranducers indicated in the uploaded file 'Instrumentation_Scheme_v1.0.0.pdf' are provided. </p> <p>The properties of the accelerometers are:</p> <ul> <li>Type: M3701-series</li> <li>Manufacturer: PCB Piezotronics, Inc.</li> <li>Measurement range: +/- 3g</li> <li>Frequency range: 0-500 Hz</li> <li>Sensitivity: 900 mV/g</li> <li>Resolution: 2.2e-5g</li> <li>Noise: 1 µg/Hz<sup>-0.5</sup></li> </ul> <p>The properties of the displacement tranducers are:</p> <ul> <li>Type: LZW-M-500</li> <li>Manufacturer: WayCon Positionsmesstechnik GmbH</li> <li>Measurement range: +/- 250 mm</li> <li>Linearity: +/- 0.05%</li> <li>Repeatability: 0.01 mm</li> <li>Displacement force: ≤15 N</li> <li>Displacement speed: ≤5 m/s</li> </ul> <h2>Files:</h2> <ul> <li>Column_Base_Anchorage_v1.0.0.pdf <ul> <li>Photo of the column-base anchorage.</li> </ul> </li> <li>Data_v1.0.0.zip <ul> <li>Contains all data files according to the load protocols.</li> <li>The experimental data is provided as .csv files for each load protocol. </li> </ul> </li> <li>Instrumentation_Scheme_v1.0.0.pdf <ul> <li>.pdf file illustrating the sensor placements on the test structure.</li> </ul> </li> <li>Load_Protocols_v1.0.0.pdf <ul> <li>.pdf file listing all load protocols applied to the structure.</li> </ul> </li> <li>References_v1.0.0.bib <ul> <li>Contains a bibtex reference with the associated publications.</li> </ul> </li> <li>Shake_Table.jpg <ul> <li>Photo of the shaking table without any specimen.</li> </ul> </li> <li>Test_Structure_v1.0.0.pdf <ul> <li>Photo of the shaking table including the test structure.</li> </ul> </li> <li>Test_Structure_Sketch_v1.0.0.pdf <ul> <li>.pdf file illustrating the test structure.</li> </ul> </li> <li>Time_Histories_v1.0.0.pdf <ul> <li>.pdf file including plots of the measurement data.</li> </ul> </li> </ul> <h2>File format of the datasets:</h2> <p>The data is stored in .csv files, where each file contains the following columns (see also 'Instrumentation_Scheme_v1.0.0.pdf'):</p> <ul> <li>Time (s): Time in seconds since the start of the test (time step equals 0.0025 s).</li> <li>Acc_0 (m/s2): Acceleration signal measured in m/s<sup>2</sup> on the shaking table in the direction of excitation (Axis A-A).</li> <li>Acc_1 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the first floor in the direction of excitation (Axis A-A).</li> <li>Acc_2 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis A-A).</li> <li>Acc_L (m/s2): Acceleration response of SDOF I measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_F (m/s2): Acceleration response of SDOF II measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_H (m/s2): Acceleration response of SDOF III measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_G (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis B-B).</li> <li>Acc_J (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the transverse direction of excitation (Axis B-B).</li> <li>Dis_0 (mm): Displacement signal measured mm on the shaking table in the direction of excitation (Axis A-A).</li> <li>Dis_1 (mm): Displacement response of the structure measured in mm on the first floor in the direction of excitation (Axis A-A).</li> <li>Dis_2 (mm): Displacement response of the structure measured in mm on the second floor in the direction of excitation (Axis A-A).</li> </ul> <p>These data files can easily be uploaded using the pandas library in Python. For example by:</p> <pre><code>import pandas as pd df = pd.read_csv('1_IM_4mm.csv') time = df["Time (s)"] acc_0 = df["Acc_0 (m/s2)"] dis_0 = df["Dis_0 (mm)"]</code></pre> <h2>Contact:</h2> <p>Please send your enquiries regarding the shaking table to <a href="dynamics@lbb.rwth-aachen.de">dynamics@lbb.rwth-aachen.de</a>. Further information can be found on our <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">website</a>.</p> <h2>Usage/License:</h2> <ul> <li>The data is licensed under CC BY-SA 4.0.</li> <li>If you have used our data and are publishing your work, we ask you to please reference both <ul> <li>this database by its DOI, and</li> <li>any publication that is associated with the experiments. See the "References_v1.0.0.bib" for the associated publication references.</li> </ul> </li> </ul> <h2>Fundings:</h2> <ul> <li>Deutsche Forschungsgemeinschaft - <em>Grant number: INST 222/1161-1 FUGG</em>. Einaxialer Schwingtisch für dynamische Modell- und Bauteilversuche.</li> <li>Bundesministerium für Bildung und Forschung - <em>Grant number: 03G0892A</em>. ROBUST – Nutzerorientiertes Erdbebenfrühwarnsystem mit intelligenten Sensorsystemen und digitalen Bauwerksmodellen – Entwicklung Installation und Anwendung von sensorbasierten Monitoringsystemen mit BIM-Integration zur Echtzeit-Schadenerkennung in kritischen Infrastrukturen.</li> </ul>
Dataset: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches
<p>This is the dataset accompanying the submission of the manuscript: Label-free detection of methicillin resistance in Staphylococcus aureus using different Raman-spectroscopy approaches in the journal Microbiology Spectrum.</p> <p>The data description is the following:</p> <p>Strains<br> 16859MRSA= Strain AUSTR-07-16859 MRSA<br> 16859MSSA= Strain AUSTR-07-16859 MSSA<br> CC8MRSA= Strain 08V15773<br> CC8MSSA= Strain MRSA2010-174<br> AUSTR05MRSA= Strain AUSTR-05-15441 MRSA<br> AUSTR05MSSA= Strain AUSTR-05-15441 MSSA<br> CC361MRSA= Strain UAE-Abu Dhabi-020<br> CC361MSSA= Strain UAE-Dubai-80-MS 1368.9/09</p> <p>Datasets<br> UVRR: UV-Resonance Raman with 244 nm excitation on bulk samples, calibration standard Polystyrene, measurements were time series of 10 consecutive spectra, for each strain and batch 25 time series were collected from 3 different slides<br> 532nm: Single cell analysis with 532nm excitation, calibration standard 4AAP, one spectrum per bacterial cell was collected<br> 785nm: Bulk analysis of bacterial colonies using 785 nm excitation and a Raman fibre probe, calibration standard 4AAP, bulk analysis, individual spectra of colonies were collected</p> <p>Data structure is in the metadata files.<br> Individual spectra are in the folders sorted by the date they were measured.</p>
Mechanically Resistant Poly(N-vinylcaprolactam) Microgels with Sacrificial Supramolecular Catechin Hydrogen Bonds
<p>Original data corresponding to the plots of Figures 2, 4, 5 of the manuscript and S1-S16 of the Supporting Information in *.csv format and raw data for NMR measurements.</p>
Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain
<p><strong>Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup> and James Irving<sup>3</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p><sup>3</sup> Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>Electrical Resistivity Tomography (ERT) profiles were collected around the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). All data were collected with a <a href="http://www.iris-instruments.com/syscal-pro.html">Syscal Pro</a> Switch 48 from Iris Instruments, using an array of maximum 48 electrodes with a spacing between 1 and 10 meters. For each line, measurements were performed using a Dipole-Dipole (dd) and a Wenner-Schlumberger (ws) electrode configuration.</p> <p>All ERT lines locations can be visualized in <em><strong>ERT_map_lines_2019-2021.jpg</strong>.</em></p> <p>A result overview can be vizualized in <em><strong>ERT_allResults_3Doverview.png</strong>.</em></p> <p><strong>Data Structure</strong></p> <p>Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses.</p> <ul> <li><em><strong>1_createInput_prosys_to_pygimli.ipynb</strong></em> : Transforms the raw data from Syscal Pro (exported with <a href="http://www.iris-instruments.com/download.html">ProsysII</a> software as .csv) to a processed .dat file formated for inversion using the <a href="https://www.pygimli.org/">pyGIMLi</a> library.</li> <li><em><strong>2_ERT_inversion.ipynb</strong></em> : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> <p>In <strong>ERT_data.zip</strong>, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains :</p> <ol> <li><strong>GPS </strong>: folder with electrodes coordinates for each ERT line</li> <li><strong>inputGiMLi</strong> <ul> <li><strong>prosys_csv</strong>: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li><strong>input_ERT </strong>: stores the processed .dat file. (created from notebook 1)</li> <li><strong>results_lambda</strong> : contains a .png image with the inversion results using different values of the regularization parameter lambda used to assess the sensitivity of the inversion results (over/underfitting). Analysis is performed for each line and each electrode configuration (dd or ws). (created from notebook 2)</li> <li><strong>results_final</strong> : contains a .png image with the final inversion results for each line and electrode configuration (dd or ws) using the optimal lambda parameter only (all arrays are shown from East to West). (created from notebook 2)</li> <li><strong>vtk</strong> : contains a .vtk file for each final results for 3D vizualization in the <a href="https://www.paraview.org/">Paraview</a> software.</li> </ul> </li> <li><strong><em>ERT_line_description_yyyy.csv</em> </strong>: a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> <p>The <strong>results </strong>folder contains :</p> <ul> <li><strong>ERT_3Dview_paraview</strong> folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li><em><strong>ERT_results_all.pdf</strong></em> : A summary of all final results for all years (similar content as <em>ERT/inputGiMLi</em><strong>/</strong><em>results_final</em> folders)</li> <li><em><strong>ERT_results_bedrock.pdf</strong></em> : Contains the vizualization of specific ERT profiles in the outwash plain and their field location . The separation between a surface layer of water-saturated sediments (resistivity <2500 Ωm) and the underlying bedrock is delimited. The likely presence of buried ice blocks (isolated blocks with resistivity >5000-10000 Ωm) is also highlighted.</li> <li><em><strong>ERT_timelapse_salt_tracer.gif</strong></em> : results of a time-lapse ERT measurement performed on 9 August 2019 to track the movement of a salt plume injected at 06 am, 9.38 meters upslope (see paper by<em> Müller et al., 2022</em> for detailed analysis). The tracer starts to appear at 12:45 at a distance of 30m on the array. Minimum resistivity is reached at between 16:45 and 17:45.</li> </ul>
Soil resistance and soil moisture data of organic, permaculture and conventional horticultural farms of Central Hungary
<p>This dataset has been produced from the PhD research of Alfréd Szilágyi supervised by Csaba Centeri and Eszter Kovács Tormáné. The study compared permaculture, organic and conventional farming systems regarding their ecosystem-service provision potential and sustainability. Multiple ecological indicators were measured in the field during the field study in 2020, and the basic datasets (soil test results; photo gallery of the studied farms with soil core sample; soil resistance and moisture; decomposition; earthworms; nematodes; soil surface fauna; pollinators; agrobiodiversity and habitat types) are uploaded in Zenodo separately to provide scientific data on permaculture systems. In this way, we hope to contribute to international efforts to evaluate the performance of agroecological agriculture alternatives. These publications also serve as supplements to the PhD thesis. For the sake of further usability of the datasets short description of the used methods is described. For further information please contact the authors.</p>
PanRes - Collection of antimicrobial resistance genes
<p><strong>PanRes database of antimicrobial resistance genes</strong></p><p>Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.</p><p>For details, please see the methods section in the following publication:</p><p><strong> "ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)</strong></p><p>Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.<br><br>A number of previously published collections of ARGs were used in the creation of PanRes (See references):</p><p><strong>ResFinder</strong> (downloaded 2023-01-20, (Bortolaia et al. 2020)),<br><strong>ResFinderFG</strong> (version 2.0, (Gschwind et al. 2023))<br><strong>CARD</strong> (version 3.2.5, (Alcock et al. 2023))<br><strong>MegaRes</strong> (version 3.0.0, (Bonin et al. 2023))<br><strong>AMRFinderPlus</strong> (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))<br><strong>ARGANNOT</strong> (V6_July2019, (Gupta et al. 2014))<br><strong>The 'CsabaPal' collection</strong> (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))<br><strong>BacMet</strong> (version 1.1, (Pal et al. 2014))</p>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Soil moisture determinations by Electrical Resistivity (ERT) Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009)
Dataset AbstractLarge-scale conversion of croplands to perennial biofuel crops could substantially impact regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, these differences in crop water use are not well known due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. Data used in: https://doi.org/10.1002/vzj2.20124original data source http://lter.kbs.msu.edu/datasets/222
CEE01 The Climate Extremes Experiment (CEE): Assessing ecosystem resistance and resilience to repeated climate extremes at Konza Prairie
Climate extremes, such as drought, are increasing in frequency and intensity, and the ecological consequences of these extreme events can be substantial and widespread. Yet, little is known about the factors that determine recovery (or resilience) of ecosystem function post-drought. Such knowledge is particularly important because post-drought recovery periods can be protracted depending on drought legacy effects (e.g., loss key plant populations, altered community structure and/or biogeochemical processes). These drought legacies may alter ecosystem function for many years post-drought and may impact future sensitivity (both resistance and resilience) to climate extremes. With forecasts of more frequent drought, there is an imperative to understand whether and how post-drought legacies will affect ecosystem response to future drought events. To address this knowledge gap, we experimentally imposed over an eight year period two extreme growing season droughts, each two years in duration followed by a two-year recovery period, in annually burned tallgrass prairie.
ESM01 Fire and grazing modulate the structure and resistance of plant-floral visitor networks in a tallgrass prairie
Data from the study: Welti, E.A.R. and Joern, A. 2017. Fire and Grazing modulate the structure and resistance of plant-floral visitor networks in a tallgrass prairie. Oecologia 186: 447-458. EMS011 dataset contains counts of blooming inflorescences of plant species on 12 Konza watersheds in June-July of 2014; ESM012 dataset contains associations between flower-visiting insects and insect-pollinated flowering plants on 12 Konza watersheds collected in May-July of 2014; ESM013 dataset describes insects belonging to the orders of Coleoptera, Diptera, Lepidoptera and Hymenoptera collected in pantrap transects on 12 Konza watersheds collected in June - July of 2014.
ScienceDex guides
Understand access before you commit
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.