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800 results for “mixtures”
Long-term monitoring of seven high marsh plant mixtures on Sapelo Island GA
We set up plots (1x2m) in 1996 in mixtures or on vegetation borders at three sites. We set up 16 plots for each of seven mixtures - Marsh Landing (Juncus-Meadow, Spartina-Meadow, and Spartina-Batis mixtures), Teal Boardwalk (Juncus-Spartina mixture) and Shell Hammock (Juncus-Spartina, Juncus-Meadow, and Spartina-Meadow mixtures). Half were watered with fresh water 3 times per week (W) and half were controls (C). Watering stopped in April 2000. Plots were monitored to document changes in plant community composition (percent cover) over time.
Dataset of "Towards 2D van der Waals Entropy Mixture MX2 (M=Mo,W; X=S,Se,Te) for Hydrogen Evolution Electrocatalysis"
<p>High-entropy alloys have emerged as a class of materials, offering unique properties due to their irregular and randomized arrangement of multiple elements in an ordered lattice. This concept has been extended to two-dimensional (2D) van der Waals materials, including transition metal dichalcogenides (TMD), which exhibit promising applications in electrocatalysis. In this work, we have explored the synthesis of entropy mixture crystals (TMDmix) involved the chemical vapor transport of five individual elements, Mo and W as metal elements, S, Se, and Te as chalcogenide elements, resulting in a crystalline structure with a controlled composition Mo0.56W0.44(S0.33Se0.35Te0.32)2, with an estimated ΔSmix of 0.96R. When observed along the [001] zone axis, STEM HAADF images indicate the presence of the different crystal phases of the 2D TMDs (1T, 2H, and 3R). Our findings demonstrate the potential of the entropy TMDmix materials as catalysts for the hydrogen evolution reaction, as an alternative to noble metal-based catalysts. To maximize the potential of TMDmix, we chose the chemical exfoliation with the resulting material being subdivided into size groups, big and small according to their lateral size. In acidic medium, the lowest overpotential of 127 mV and Tafel slope of 79 mV/dec were obtained for the exfoliated sample with a small lateral size (exf-TMDsmall).</p>
Removal of Aqueous Uranyl and Arsenate Mixtures by Natural Limestone and Hydroxyapatite Precipitates, Rio Paguate, NM, 2022-2023
This dataset documents a series of laboratory batch experiments investigating the removal of aqueous uranyl (U) and arsenate (As) mixtures using natural limestone and precipitated hydroxyapatite (HAp, Ca₁₀(PO₄)₆(OH)₂) as reactive materials. The main objective of the study was to address the challenge of simultaneous removal of uranyl cations and arsenate oxyanions by using mineral-based adsorbents, such as limestone. The precipitation of HAp enhanced As removal while maintaining high uranium immobilization efficiency.The archived data include measurements of aqueous U and As at trace-level concentrations under varying experimental conditions, including pH (ranging from 7 to 11), initial contaminant concentrations (0.05–1 mM), and the addition of calcium (Ca²⁺) and phosphate (PO₄³⁻) to promote HAp precipitation. Experiments were conducted in triplicate to ensure reproducibility, and solid-phase characterization data (from pXRD, SEM/EDX, and electron microprobe analysis) are also included to support the interpretation of removal mechanisms. A key finding revealed from the data is that near-complete removal of U (>97%) with As removal between 30 and 98% were achieved under pH conditions around 9. This dataset provides a comprehensive record of solution chemistry and treatment performance, serving as a fundamental resource for evaluating the effectiveness of natural mineral-based approaches for remediating co-contaminated waters.
Oxidation of Ammonia/Methanol Mixtures in a plug-flow reactor with TOF-MS at 373-973K
<p>TOF-mass spectrometric measurements with a plug-flow reactor for ammonia/methanol gas mixtures (neat, 10% and 20% methanol in ammonia for equivalence ratios 1 and 2)</p> <ul> <li>temperature range: 373-973 K</li> <li>pressure: 3 bar</li> <li>dilution: 98 %</li> </ul> <p>Dataset described, analyzed and discussed in: A. Welp, C. Rudolph, B.R. Giri, K.P. Shrestha, R. Verma, F. Mauss, and B. Atakan, Oxidation of Ammonia Methanol Blends: An Experimental and Kinetic Modeling Study, 2025, accepted for publication in Combustion and Flame.</p>
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Inter-Chemical Correlation results for the study: HHEARx2017-1740 (Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth)
Title: Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth <br>Species: Homo sapiens <br>Number of samples: 1423 <br>Number of named analytes: 20 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=19 <br>
Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior
<p>The datasets used in the paper "Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior". A detailed description of these datasets is available at https://github.com/jaydu1/VITAE/tree/master/data.</p>
SoA of existing barriers against the measuring of H2NG mixtures or pure H2
<p>Deliverable D1.2 of THOTH2 project aims to investigate technical barriers and limitations of hydrogen injection in existing gas grids with a focus on measuring devices. Injecting hydrogen into existing gas networks requires several actions to assess the content of hydrogen in the H2NG mixture that can be transported through gas pipelines. One of the areas that needs to be evaluated is the field of gas quality and quantity measurements.</p> <p>The Task 1.2 "Technical and performance barriers and limitations" aimed to identify barriers and gaps related to measurement devices such as gas meters, volume converters, pressure and temperature transducers, process gas chromatographs, dew point transducers, and leak detectors. By defining this measurement area, it allows identifying gaps and barriers concerning custody transfer measurements (gas volume and heating value).</p> <p>Due to the different physicochemical properties of hydrogen and methane, the identification of barriers and considerations includes not only the measurement capabilities of devices for H2NG mixtures but also the resistance of design solutions to the effects of hydrogen. The assessment of the state-of-the-art technology was conducted based on a literature review, including the outcomes of projects such as "HyDeploy 2 Project" and "HyWay 27: hydrogen transmission using the existing natural gas grid?". The literature review was complemented by feedback gathered from surveys sent to manufacturers of measurement devices, selected based on the results of Task 1.1. The analyses conducted allowed identifying the following gaps and barriers:</p> <ul> <li>In the area of gas composition analyzers: there are solutions available that allow measuring the composition of H2NG mixtures with up to 100% hydrogen content. However, these solutions are not currently utilized by Transmission System Operators (TSOs) and Distribution System Operators (DSOs), since they entered in the market recently.</li> <li>In the area of dew point transducers: there are solutions resistant to hydrogen up to 20% content, and they are partially used by TSOs and DSOs.</li> <li>In the area of leak detectors: there are solutions that can be used by operational services on gas networks transporting H2NG mixtures. These detectors allow measuring hydrogen content in the air from 0 to 100% of the lower explosive limit. This range is sufficient to ensure safe network operation. However, such leak detectors are not currently employed by TSOs and DSOs.</li> </ul>
Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers
<p>Data archive corresponding to the publication "Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers " by O. Bouzid <em>et al</em>., Polymers <strong>16</strong>, 2311 (2024). </p> <p>Preprint available at: 10.20944/preprints202407.0786.v1</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All system configurations have been generated and successively analyzed by the Simu-D software.</p> <p> </p> <p>This research was funded by MICINN/FEDER (Ministerio de Ciencia, Innovación y Universidades, Fondo Europeo de Desarrollo Regional), grant number “PID2021-127533NB-I00”, by the scholarship program from the Algerian Ministry of Higher Education and Scientific Research and by UPM and Santander Bank, “Programa Propio UPM Santander”.</p>
FTICR MS data for standards and mixtures for quantitative peak intensity investigation
<p>This upload contains raw (Bruker .d format) FTICR mass spectrometry data (direct infusion, negative mode ESI) for standards in different mixtures and matrices for the purposes of investigating the (non)quantitative nature of the data. <br>Processed data (Excel format), and Python scripts used for data processing are also included. </p> <p>Note - the Python scripts used CoreMS version prior to V2.0 for analysis - to re-run these scripts with a more recent release likely requires syntax updates. </p>
Data for: Attractive solution of binary Bose mixtures: Liquid-vapor coexistence and critical point
<p>Path-integral Monte-Carlo results for a balanced Bose mixture with attractive interspecies interaction. In the files named "press_*" we provide the pressure data, presented in figures 1, 2, and S1, and then used to extract the coexistence regions. In the files named "coexistence_region_*" we provide the data extracted using the Maxwell construction (as well as the condensate fraction for a12=-1.2a) and presented in figures 3, 4, 5 and S2.</p>
Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1
<p>This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air.</p> <p>The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings.</p> <p>The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images.</p> <p>This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs.</p> <p>Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”.</p> <p>The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission.</p> <p>CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good.</p> <p>Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org</p> <p>Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317.</p> <p>This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.</p>
qPCR raw datasets on the assessments of cover crop monocultures and mixtures improving the rhizosphere bacterial abundance and functionality through rerooting
<p>Quantitative PCR (qPCR) raw data includes the source data that corresponds to the the counts of 16S rRNA gene copies per gram of soil for different variations, as discussed in the research article - "Cover crop monocultures and mixtures improve the rhizosphere bacterial abundance and functionality through rerooting". The copy number of the 16S rRNA gene per gram of soil was quantified by SYBR® Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilized in amplicon sequencing were used in qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analyzed in triplicate. A PCR amplicon of the <i>Escherichia coli</i> V3 region was used as standard. Each reaction of 20 µL contained 1 µL of template DNA, the forward primer 341F (Muyzer et al., 1993), the reverse primer 518R (Muyzer et al., 1993), and Luna® Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95 °C, followed by 40 cycles of denaturation at 95 °C for 15 s and extension at 60 °C for 30 s. The melting curve was recorded in the temperature range of 60 °C to 95 °C. The 16S rRNA gene copy numbers per gram of soil were calculated using the standard curve method and then normalized against the standard (Adelowo et al., 2018). The average efficiency value was 100.77 ± 3.15 %. The absolute copy numbers for each bacterial phylum were calculated by multiplying the qPCR values by the relative abundance values in percent obtained from the 16S rRNA gene sequencing analyses.</p>
SEM images of SiO2 and juniper charcoal powders (pure samples and intimate binary mixtures).
<p><strong>Summary:</strong><br>These images are those from SiO2 and juniper charcoal (JChc) powder samples observed with a SEM. The powders were obtained from commercial sources and these samples were prepared at the Bern University (Switzerland) as part of the D-A-CH/CoPhyLab project (https://www.cophylab.space/index.php?id=home)<br><br><strong>Details:</strong><br>¤ SEM images from the sample of pure SiO2:<br> 001-St5607t0_00.tif<br> 002-St5607t0_01.tif<br> 003-St5607t0_03.tif:<br><br>¤ SEM images from the sample of pure juniper charcoal powder:<br> 004-St5607t6_00.tif<br> 005-St5607t6_01.tif<br> 006-St5607t6_04.tif<br> 007-St5607t6_05.tif<br> 008-St5607t6_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 90% SiO2 - 10% JChc by mass:<br> 009-St5606t1_00.tif<br> 010-St5607t1_01.tif:</p><p>¤ SEM images from the sample of the intimate mixture with 70% SiO2 - 30% JChc by mass:<br> 011-St5607t2_00.tif<br> 012-St5607t2_02.tif<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample at lignin fragment peppered with smaller JChc and SiO2 particles and agglomerates:<br> 013-St5607t2_03.tif:<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample with apparent large fragment of lignin structure:<br> 014-St5607t2_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 50% SiO2 - 50% JChc by mass:<br> 015-St5607t3_00_St5606t3_00.tif<br> 016-St5607t3_03.tif<br><br>¤ SEM images from the sample of the intimate mixture with 30% SiO2 - 70% JChc by mass:<br> 017-St5607t4_00.tif<br> 018-St5607t4_01.tif, <br><br>¤ SEM images from the sample of the intimate mixture with 10% SiO2 - 90% JChc by mass:<br> 019-St5607t5_00.tif<br><br><strong>Addendum:</strong><br>These SEM images are associated with the spectroscopic and photometric data available at the following addresses:<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200723_000<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200813_000</p>
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K
<div><strong>Summary:</strong></div> <div>Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them.</div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Characteristics of bituminous binder, CSV raw data</div> <div> <ul> <li>bituminous binder.csv</li> </ul> </div> <div>Grading curves of tested asphalt mixtures</div> <ul> <li>AML16 Grading curves.csv</li> <li>AMP22 Grading curves.csv</li> </ul> <div>Volumetric characterizations of AML16 and AMP22 mixtures</div> <ul> <li>AML16 Volumetric characterizations.csv</li> <li>AMP22 Volumetric characterizations.csv</li> </ul> <div>Outcomes of the 4PBT experimental trial carried out on AML16 and AMP22 mixtures</div> <ul> <li>AML16 Stiffness Modulus 4PB.csv</li> <li>AMP22 Stiffness Modulus 4PB.csv</li> </ul>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
MALDI-TOF spectra of bovine, caprine and ovine milk and their mixtures
<p>In this dataset we provide MALDI-TOF spectra of bovine milk in caprine and ovine milk. The dataset contains normalized spectra of pure milk samples and their mixtures. <strong>Different level of adulteration (0.5%, 1%, 5%, 10%, 20%, 40%, 60%, 80%)</strong> were analyzed throughout the lactation period of goat and sheep. Two different ranges of peptide-protein spectra <strong>(500–4000 Da; 4–20 kDa)</strong> were used. Exported spectra are in TXT files (<em>m/z</em> and intensity) for processing in other software (ex. mMass). Acquisition methods are included.</p> <p>MALDI-TOF MS analysis was performed using an Autoflex Speed (Bruker Daltonics, Germany) equipped with a SmartBeam<sup>TM</sup> II laser (355 nm) and flexControl software (version 3.4 Build 135, Bruker Daltonics, Germany).</p> <p> </p> <p>The dataset is a part of Supplementary file for the manuscript:</p> <p><em><strong>Evaluation of MALDI‐TOF MS technology in small ruminants’ milk adulteration using raw bovine milk</strong></em> by L. Rysova, P. Cejnar, O. Hanus, V. Legarova, J. Havlik, H. Nejeschlebova, I. Nemeckova, R. Jedelska, M. Bozik, submitted to <em>Journal of Dairy Science</em> (Manuscript ID JDS.2021-21396) on 08-Oct-2021.</p>
Dataset accompanying Nölke et al. 2022. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Scientific Reports
<p>This repository contains biomass and nitrogen yield data as well as data on diversity effects used by Nölke et al. in an article published in Scientific Reports (2022).</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original research article.</p>
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.