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3,871 results for “quantitative”
Data from: The Fezouata Shale Formation biota is typical for the high latitudes of the early Ordovician – a quantitative approach
<p>The Fezouata Shale Formation has dramatically impacted our understanding of early Ordovician marine ecosystems before the Great Ordovician Biodiversification Event (GOBE), thanks to the abundance and quality of exceptionally preserved animals within. Systematic work has noted that the shelly fossil sub-assemblages of the Fezouata Shale biota are typical of open-marine deposits from the Lower Ordovician, but no studies have tested the quantitative validity of this statement. We extracted 491 occurrences of recalcitrant fossil genera from the Paleobiology Database to reconstruct 31 sub-assemblages, to explore the paleoecology of the Fezouata Shale and other contemporary, high-latitude (66°S – 90°S) deposits from the Lower Ordovician (485.4 Ma – 470 Ma) and test the interpretation that the Fezouata Shale biota is typical for an Ordovician open-marine environment. Sørensen's dissimilarity metrics and Wilcoxon tests indicate that the sub-assemblages of the Tremadocian-aged lower Fezouata Shale are approximately 20 percent more heterogenous than the Floian-aged upper Fezouata Shale. Dissimilarity metrics and visualization suggests that while the lower Fezouata and upper Fezouata share faunal components, the two sections have distinct faunas. We find that the faunal composition of the lower Fezouata Shale is comparable with other Tremadocian-aged sub-assemblages from high latitudes, suggesting that it is typical for an early Ordovician open-marine environment. We also find differences in faunal composition between Tremadocian- and Floian-aged deposits. Our results corroborate previous field-based and qualitative systematic studies that concluded that the shelly assemblages of the Fezouata Shale are comparable with those of other Lower Ordovician deposits from high latitudes. This establishes the first quantitative baseline for examining the composition and variability within the assemblages of the Fezouata Shale which will be key to future studies attempting to discern the degree to which it can inform our understanding of marine ecosystems just before the start of the GOBE.</p>
In vivo parameter maps for: Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
<p>Quantitative magnetization transfer and relaxometry maps as described in the Paper <em>Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools</em>.</p> <p>.</p>
Quantitative results of the analysis of novel ossicle particles used in mandible bone regeneration
<p>Dataset corresponding to the results of the characterization analysis of novel holothurian ossicle biomaterials. These biomaterials were evaluated at three levels:</p> <p>1) Ex vivo analysis to determine thr potential cytotoxic effects of these biomaterials on human fibroblasts using LIVE/DEAD and quantification of DNA released to the medium.</p> <p>2) In vivo analysis to determine the potential systemic effects of these biomaterials grafted subcutaneously in laboratory rats.</p> <p>3) Histochemical and immunohistochemical analysis to determine the potential effects of these biomaterials on mandible bone regeneration.</p> <p>These results correspond to the publication entitled "<span>EVALUATION OF HOLOTHURIAN OSSICLES AS A BIOLOGICAL BIOMATERIAL FOR MANDIBULAR BONE REGENERATION</span>".</p>
Quantitative results of the analysis of human native and bioengineered tissues corresponding to the work "Histological, histochemical and immunohistochemical characterization of NANOULCOR nanostructured fibrin-agarose human cornea substitutes generated by tissue engineering"
<p>Dataset containing the quantitative results of the histochemical and immunohistochemical analysis of the following human tissues:</p> <ul> <li>Control native cornea (CTR-C)</li> <li>Control native limbus (CTR-L)</li> <li>Artificial cornea generated by tissue engineering (HAC)</li> </ul> <p>Each tissue type was subjected to histochemical and immunohistochemical analyses and results were quantified using ImageJ software to determine average intensities and area fractions corresponding to positive staining signal for each marker.</p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
Figure 1 in Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data-poor organisms like cetaceans
Figure 1. Depiction of the divergence of lineages with four times (T1–T4) chosen to illustrate different levels of biological organization. At T1 the yellow lineage is found across the distribution and although there are likely Demographically Independent Populations (DIPs) that differ in frequencies of the blue, yellow, and red lineages, there are no discontinuities. At T2 some lineages may be diagnosable but likely do not yet appear to be separate lineages. At T3 three groups (the blue/green, yellow, and orange/red lineages) meet the subspecies definition (they are diagnosable and appear to be diverging separately). The divergence level is not sufficient that reconvergence can be ruled out. Between T3 and T4, barriers to gene flow change such that the yellow lineage comes into contact with the blue/green and red-dominated lineages. Blue has diverged in a manner by which gene flow does not resume and the green/yellow lineage dies out. The yellow lineage reconverges and persists alongside the red lineage with a small level of gene flow (orange). At T4 the blue lineage is a species evolving separately from the yellow/red species. The yellow/red species has two subspecies that are both diagnosable and partially diverged.
Figure 3 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 3. Flow diagram for subspecies delineation using combined quantitative and qualitative standards. The threshold values assume the user is evaluating a case relying on mtDNA control region data. Percent Diagnosable (PD) is the smallest strata-specific correct classification score in a given comparison (e.g., PD50 in two-strata comparisons in Archer et al. 2017). The second box in the second row (other evidence to meet subspecies definition) allows for subspecies delineation when both conditions are not met using mtDNA. This box could be used either for the case when one condition is met and one unmet or when both just barely miss meeting the standards. For example, consider the case with PD <95% and dA> 0.004. Diagnosability could be achieved with morphological data or nuclear data that are sufficient for subspecies but not for full species.
Figure 2. A in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 2. A comparison of the pairs of populations (red triangles), subspecies (green squares) and species (blue circles) estimated by Rosel et al. (2017a). Net nucleotide divergence (dA) is shown on a natural log scale to better illustrate differences between the pairwise comparisons at low levels of divergence. Bars show the central 95th-pecentile of the estimate distributions. The solid vertical line at dA = 0.020 delimits all but one species and correctly excludes all subspecies pairs. The vertical dashed line at dA = 0.004 delimits all populations from the higher taxonomic levels and correctly delimits seven of eleven subspecies. The horizontal dashed lines are two potential thresholds for percent diagnosable (80% and 95%) that are discussed in the text.
Data set from: Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis?
<p><strong>Abstract:</strong> This work investigated the capability of quantitative laboratory X-ray Absorption Fine Structure Spectroscopy (lab-XAFS) via Linear Combination Fitting (LCF) of reference spectra in comparison with quantitative X-ray diffraction (XRD) and Mössbauer spectroscopy. While lab-XAFS already show good results when performing LCF with significant different spectra of the species to be identified, the method is challenging when the reference spectra and possibly species in the sample are very similar as it is the case for α-Fe<sub>2</sub>O<sub>3</sub>, γ- Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub>. For this investigation an iron oxide mineral with origin from Mexico (here named Mexican Magnetite) with different iron oxide phases was used and measured using all three methods.</p> <p> </p> <p>This data set contains the raw data of the work “<em>Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis? Critical evaluation using the example of a natural iron ore</em>” of XAFS, XRD and Mössbauer measurements. This includes XAFS, Mössbauer and XRD spectra of the reference materials α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub> and the sample Mexican magnetite, the XAFS spectra of the reference material γ- Fe<sub>2</sub>O<sub>3</sub> and the XAFS, XRD and Mössbauer spectra of three different α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures.</p> <p> </p> <p><u>Sample information/sample list</u></p> <p><strong>sample/references:</strong> The sample and the corresponding short cut name used in the data files is listed. Furthermore the method the sample was measured with is also listed.</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong> Sample/reference</strong></p> </td> <td> <p><strong>Measured with</strong></p> </td> </tr> <tr> <td> <p>MexicanMagnetite</p> </td> <td> <p> Iron oxide mineral with origin in Mexico</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe2O3</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe3O4</p> </td> <td> <p>Fe3O4 / Magnetite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe</p> </td> <td> <p>Iron powder</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-alpha</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-gamma</p> </td> <td> <p>Fe2O3-gamma / Maghemite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>Mixture of 30 % Fe2O3-alpha/ 70 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>Mixture of 50 % Fe2O3-alpha/ 50 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>Mixture of 70 % Fe2O3-alpha/ 30 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Mixtures ratios:</strong> The prepared Fe2O3-Fe3O4 model mixtures with the weight-in ratios and the actual achieved mass percentage ratio between the two iron species, taken impurities of the used materials into account, are listed below. The short cut name is the name used in the data files (see table above).</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong>Actual achieved weigh-in ratios</strong></p> <p><strong>m(Fe2O3)/m(Fe3O4)*</strong></p> </td> <td> <p><strong>Actual achieved mass percentage ratios ωrel(Fe2O3) / ωrel(Fe3O4)</strong></p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>0.31380 g / 0.7059 g</p> </td> <td> <p>31.8 / 68.2</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>0.5140 g / 0.5174 g</p> </td> <td> <p>50.6 / 49.4</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>0.7037 g / 0.3041 g</p> </td> <td> <p>70.5 / 29.5</p> </td> </tr> </tbody> </table> <p>*the given masses here, ar the masses of the materials of the mixtures before sampel prepration. For the sample prepration the mass applied on the tape or mixed with wax is about 5-10 mg.</p> <p><u>Spectrometer Specifications</u></p> <p><strong>XAFS:</strong> The experimental setup for the laboratory XAFS measurement is based on the Highly Annealed Pyrolytic Graphite (HAPG) von Hámos spectrometer with the use of a cylindrically shaped crystal.</p> <p>As detector unit the pixelated X-ray hybrid-CMOS detector Dectris Eiger2 R 500k was used. The area of detection is 77.3 mm x 38.6 mm with a pixel size of 75 µm x 75 µm. The X-ray source was a water-cooled micro focus X-ray tube with molybdenum as anode material, a power of 30 Watt optimised at 15 kV and a spot size of 70 µm.</p> <p><strong>Sample preparation</strong>: α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures and the sample Mexican magnetite were applied on adhesive tape, sliced in 1cm x 1cm pieces characterized with XRF to determine the iron content as [<em>Q</em>] = mg/cm² and then stacked by taking the iron content of each slice into account to achieve an absorption of <em>µ*Q</em> of about 1 at the edge.</p> <p>The γ- Fe<sub>2</sub>O<sub>3</sub> and also the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures were prepared as Pellet. Here the sample material was mixed with Hoechst Wax C in a ratio of 1:6, mixed in a vortex shaker and then pressed with a hydraulic press with a Pellet diameter of 13 mm. The amount of the wax/sample powder material was weight before inserting in the press to the amount of <em>Q</em> to achieve a <em>µ*Q</em> of about 1 with a 13 mm Pellet.</p> <p>Shifts of the energy axis as well as a widening or compression of this axis could be present when comparing the data with other data sets of other spectrometer or synchrotron radiation facilities, since no precise energy calibration was carried out due to the reason that the samples were compared to the measured references and would have the same shift, widening or compression.</p> <p> </p> <p><strong>XRD:</strong> Two different commercial XRD set ups have been used. For the Mexican magnetite the Benchtop XRD spectrometer Bruker D2Phaser with a Cobalt X-ray source and a SSD160 detector (active length = 12 mm) was used. The measurement range was 10°- 90° 2theta with 0.014° step size and 4.8 s/step, resulting in a total measurement time of 8h. During the measurement the sample was rotated with 10 rpm. The sample was filled in PMMA-holders (Ø 2.5 mm) using the top-loading technique. The analysis was carried out using a 1-mm fixed divergence slit, a 2.5° primary and a 4° secondary soller collimator, a fixed knife edge (3 mm above the sample surface), and an Fe Kβ filter (2.5).</p> <p>For the X-ray diffraction measurements of the α-Fe2O3/Fe3O4 mixtures and the pure references a Panalytical X’Pert PRO diffractometer with a Bragg-Brentano setup was used. The diffractometer operates with a Cu anode and without a monochromator (Cu-Kalpha radiation) at 40 kV and 30 mA. The diffraction data were obtained over a measurement range of 10–120° 2theta. Samples were applied flat on a cut-off Si wafer attached to the sample holder.</p> <p><em> </em></p> <p><strong>Mössbauer:</strong> Mössbauer spectroscopy was performed at a MIMOS II type spectrometer with a <sup>57</sup>Co source (in rhodium matrix). For the analyses the <sup>57</sup>Fe-γ-line E = 14.4 keV was used and α-iron (α-Fe foil) was applied for the velocity calibration before the samples were analyzed. The samples were prepared in plastic powder sample holders and measured in transmission mode at room temperature. The measurement time varied between 12 h and 120 h depending on the sample.</p> <p> </p> <p><strong>Information on data sets</strong></p> <p>XAFS - this folder contains the XAFS spectra as intensity file with I0 (without the sample) and the It (transmission signal through the sample) for each sample. Multiple samples (It) share the same I0 and are therefore in the same data set. The Number in the filename between “XAFS“ and “data-set..” is the date of the measurement in the following format: YYYY_MM_DD. The first column in each file is the energy in unit eV. The abbreviation “WP” after each sample name in the header means “<strong>W</strong>ax <strong>P</strong>ellet” and indicates that the measurement was performed on a sample prepared as a wax pellet, the number (WP<strong>1</strong>) indicates the number of the pellet. Two pellets of each mixture were prepared to investigate the influence of the sample preparation. If the sample name is missing “WP#” the sample was prepared on adhesive tape as described above. The information on the contents of each data set as well as the measurement time (t = #h) for each It of the sample/reference can be found in data_dictionary_v2.txt.</p> <p>The intensity is normalized to counts per 1800 seconds in a 0.25 eV (for data-set-1) and 1 eV (for data-set-2, data-set-3 and data-set-4) energy interval with the indicated central bin energy.</p> <p> </p> <p>XRD - this folder contains the raw intensity files over 2theta (ASC-file). Each sample has its own file with the first column for the 2theta in unit degree and the second column for the measured intensity.</p> <p>The Number in the file name between XRD and sample name (e. g. Fe2O3, 30-70) is the date of the measurement in the following format: YYYY_MM_DD.</p> <p> </p> <p>MOESSBAUER - this folder contains the recoil Lorentz site analysis fit data of the samples. The files consist of the observed intensity (Iobs) over the velocity (v (mm/s)), including the calcucalted intensity (Icalc) and the fits of the subspectra (Sextet Site 1, etc. ). Each sample has it owns file. While the references substances <br>Fe2O3 and Fe3O4 were measured between 2016 and 2019, the MexicanMagnetite was measured 2020. An exact measurement date can’t be determined anymore.</p> <p> </p> <p>The corresponding sample to the short cut name (e. g. Fe2O3, 30-70,..) in the files can be found above and is listed in the <em>data_dictionary.txt</em> file as well.</p> <p> </p>
Fig. 3 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 3. – Origin of introduced plant species in Burkina Faso. The majority of introduced species originates in the Americas.
Fig. 6 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 6. – Province species richness in relation to province characteristics. Species richness per province is shown dependent on 4 factors.
Fig. 1 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 1. – The provinces of Burkina Faso and their assignment to the phytogeographic zones used in this study. The classification of provinces to the PGZs is modified after WHITE (1983) and GUINKO (1984a). [1: Les Balé; 2: Bam; 3: Banwa; 4: Bazègua. 5: Bougouriba; 6: Boulgou; 7: Boulkiemdé; 8: Ganzourgou; 9: Gnagna; 10: Gourma; 11: Houet; 12: Ioba; 13: Kadiogo; 14: Kénédougou; 15: Comoé; 16: Komandjari; 17: Kompienga; 18: Kossi; 19: Koulpélogo; 20: Kouritenga; 21: Kourwéogo; 22: Léraba; 23: Loroum; 24: Mouhoun; 25: Nahouri; 26: Namentenga; 27: Nayala; 28: Oubritenga; 29: Oudalan; 30: Passoré; 31: Sanguié; 32: Sanmatenga; 33: Séno; 34: Sissili; 35: Soum; 36: Sourou; 37: Tapoa; 38: Tuy; 39: Yagha; 40: Yatenga; 41: Ziro; 42: Zondoma; 43: Zoundwéogo; 44: Poni; 45: Noumbiel]
Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin
<p>This dataset is associated with "Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin", by Mikko Partanen, Hyeonwoo Lee, and Kyunghwan Oh, Photonics Res. 9, 2016 (2021) [https://doi.org/10.1364/PRJ.433995].</p> <p>It includes data files and Matlab (R2017b) scripts to allow for the replication of the figures. The data files give the oscillator mirror position in the units of nanometers measured at the rate of 200 times per second.</p>
Semi‐quantitative metabarcoding reveals how climate shapes arthropod community assembly along elevation gradients on Hawaii Island
<p>Spatial variation in climatic conditions along elevation gradients provides an important backdrop by which communities assemble and diversify. Lowland habitats tend to be connected through time, whereas highlands can be continuously or periodically isolated, conditions that have been hypothesized to promote high levels of species endemism. This tendency is expected to be accentuated among taxa that show niche conservatism within a given climatic envelope. While species distribution modeling approaches have allowed extensive exploration of niche conservatism among target taxa, a broad understanding of the phenomenon requires sampling of entire communities. Species-rich groups such as arthropods are ideal case studies for understanding ecological and biodiversity dynamics along elevational gradients given their important functional role in many ecosystems, but community-level studies have been limited due to their tremendous diversity. Here, we develop a novel semi-quantitative metabarcoding approach that combines specimen counts and size-sorting to characterize arthropod community-level diversity patterns along two elevational gradients across two volcanoes on the island of Hawai`i. We find that arthropod communities between the two transects become increasingly distinct compositionally at higher elevations. Resistance surface approaches suggest that climatic differences between sampling localities are an important driver in shaping beta-diversity patterns, though the relative importance of climate varies across taxonomic groups. Nevertheless, the climatic niche position of OTUs between transects was highly correlated, suggesting that climatic filters shape the colonization between adjacent volcanoes. Taken together, our results highlight climatic niche conservatism as an important factor shaping ecological assembly along elevational gradients and suggest topographic complexity as an important driver of diversification.</p>
Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><strong>Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential</strong></p> <p>Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273.</p> <p>Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)*<br> 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland<br> 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland.<br> 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland <br> 4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping</p> <p> </p> <p><strong>Study and data description</strong></p> <p>Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed.</p> <p>The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds. </p> <p>The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).</p> <p> </p> <p>Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are:</p> <ul> <li><strong>additional_info_scotspine</strong>: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li><strong>manuscript_figures:</strong> Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li><strong>matlab_scripts:</strong> Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li><strong>mri_scotspine</strong>: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li><strong>radiography_scotspine: </strong>Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>).</li> <li><strong>readme.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Please see the included readme.txt for further details.</p> <p> </p> <p>(Teemu Tuomainen, Jan 25, 2022)</p>
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using 1H NMR
<p>Dataset to accompany the manuscript "Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using <sup>1</sup>H NMR"</p>
Quantitative raw data for "Large scale regional citizen surveys report" (D1.4)
<p>This dataset presents the quantitative raw data that was collected under the H2020 RRI2SCALE project for the D1.4 - “Large scale regional citizen surveys report”. The dataset includes the answers that were provided by almost 8,000 participants from 4 pilot European regions (Kriti, Vestland, Galicia, and Overijssel) regarding the general public's views, concerns, and moral issues about the current and future trajectories of their RTD&I ecosystem. The original survey questionnaire was created by White Research SRL and disseminated to the regions through supporting pilot partners. Data collection took place from June 2020 to September 2020 through 4 different waves – one for each region. Based on the conclusion of a consortium vote during the kick-off meeting, it was decided that instead of resource-intensive methods that would render data collection unduly expensive, to fill in the quotas responses were collected through online panels by survey companies that were used for each region. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the "Large scale regional citizen surveys report" (D1.4).</p>
Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study
<p><strong>Supplementary Materials: </strong>The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, <strong>Table ST1</strong> – qMIDS<sup>V2 </sup>Gene panel primer sequences; <strong>Figure S1</strong> – qMIDS<sup>V1</sup> vs qMIDS<sup>V2</sup> 384-well assay format and protocols; <strong>Figure S2.</strong> Individual target gene expression pattern in 1761 samples; <strong>Figure S3.</strong> Various statistical methods used for gene selection analysis on 1761 clinical samples; <strong>Figure S4. </strong>Diagnostic performance comparison between qMIDS<sup>V2</sup> vs qMIDS<sup>V2* </sup>(with 4 less effective genes removed from the panel of 14 target genes of qMIDS<sup>V2</sup>); <strong>Figure S5</strong>. Effect of removing individual genes from the 14-target gene panel qMIDS<sup>V2</sup> (qV2) on diagnostic test performance based on the UK patient cohort data.</p>
Quantitative Mouse Phosphoproteomic Dataset
<p>A curated and developed mouse phosphoproteomic database consisting of 10 publications that hold 33 experiments between them. This newly developed database has 136 conditions with 142,705 unique peptides and 10,052 unique UniProt ids.</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.