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Data from: Biosystematics of Platanthera bifolia s.l. (Orchidaceae): Inferences from analysis of Scandinavian population samples
<p>Over the years, various authors have (sub)divided the Eurasian moth pollinated Platanthera bifolia into several taxa. Advanced studies using multivariate morphometric analysis and/or genetic fingerprinting have all included regions where the situation appears particularly complex. With the aim to resolve variation patterns in a region where the situation seems less complex, we analysed morphometric and AFLP data from 13 Scandinavian populations using a range of uni- and multivariate statistical methods. Variation was largely continuous, though with (individuals from) short-spurred and long-spurred populations, respectively, forming loose groups. Provided that successful pollinator shifts usually occur between moth species with small difference in proboscis length, this pattern is congruent with the hypothesis that spur length in P. bifolia s.l. has mainly evolved through pollinator shifts followed by selection in response to proboscis length of the prevailing local pollinator species. Acknowledging an important adaptive role of spur length, observing that spur length was among the main contributors to morphological variation, and noting this pattern to be congruent with both AFLP patterns and habitat requirements, we advocate the formal distinction between a short-spurred and a long-spurred taxon. Adopting the operational definitions of species, subspecies and variety provided in Flora Nordica, the two taxa should be recognized as P. bifolia var. bifolia and P. bifolia var. latissima, respectively. A key to the varieties is provided.</p>
Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
<p class="List1"><span>Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from non-invasive genetic sampling of seven boreal caribou populations (<i>Rangifer tarandus caribou</i>) which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of non-independence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures were strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.</span></p>
FIGURE 5 in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 5. Maps showing the distribution of kinorhynch species along the Iberian coastline. Abbreviations: A, Atlantic Sea. M, Mediterranean Sea.
FIGURE 2. Light micrographs. A in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 2. Light micrographs. A, Campyloderes cf. vanhoeffeni, ventral view. B, Semnoderes armiger, female, ventral view. C, Centroderes spinosus, female, dorsal view. D, Echinoderes dujardinii, male, ventral view. E, Ventral detail of segments 9–11 in a female of Centroderes spinosus.
FIGURE 3. Light micrographs. A in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 3. Light micrographs. A, Pycnophyes zelinkaei, male, dorsal view. B, Pycnophyes carinatus, dorsal view. C, P. carinatus, ventral view, showing placids and the shape of the midsternal and episternal plates. D, P. zelinkaei, ventral view, showing the serrated pectinate fringe from segment 8. E, P. zelinkaei, dorsal view, showing the serrated pectinate fringe from segment 8.
FIGURE 1 in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 1. Map showing collecting areas and localities (close-up in the insets) yielding kinorhynchs along the Iberian Peninsula. Abbreviations: M, Mediterranean Sea; A, Atlantic Sea.
Data From: Patterned Dried Blood Spot Cards for Improved Sampling of Whole Blood
<p>This is the data set from all figures and tables from the manuscript "Patterned Dried Blood Spot Cards for Improved Sampling of Whole Blood", which is posted to the ChemRxiv preprint server (<a href="https://doi.org/10.33774/chemrxiv-2021-b0rpt">10.33774/chemrxiv-2021-b0rpt</a>) and currently in consideration for peer-reviewed publication elsewhere. </p>
Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates - Data and sample code
<p>This repository contains the processed data shown in the figures of the paper "<em>Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates", </em><em>Science Translational Medicine. </em>It also contains sample code for reference as a guideline to reproduce the analysis performed in the paper. </p>
Text-fig. 13. Ranges of the length of the m3 based on data from the literature, see Text-fig. 12 for explanation. in An Exceptional Large Sample Of The Early Miocene Ctenodactyline Rodent Sayimys Giganteus, Specific Variation And Taxonomic Implications
Text-fig. 13. Ranges of the length of the m3 based on data from the literature, see Text-fig. 12 for explanation.
Text-fig. 12. Ranges of the length of the dp4 based on data from the literature. Vertical scale in Ma, the samples are from the Siwaliks (black) and Turkey, Greece and Arabia (red). Type localities of species are indicated with an asterisk. The Y and Z sites are from Baskin (1996), its size ranges are composed of data from several localities and the vertical arrowed broken line indicates the age range of these sites. in An Exceptional Large Sample Of The Early Miocene Ctenodactyline Rodent Sayimys Giganteus, Specific Variation And Taxonomic Implications
Text-fig. 12. Ranges of the length of the dp4 based on data from the literature. Vertical scale in Ma, the samples are from the Siwaliks (black) and Turkey, Greece and Arabia (red). Type localities of species are indicated with an asterisk. The Y and Z sites are from Baskin (1996), its size ranges are composed of data from several localities and the vertical arrowed broken line indicates the age range of these sites.
Boardman River 2019 eDNA metabarcoding water sample data
<p>Understanding biodiversity in aquatic systems is critical to ecological research and conservation efforts, but accurately measuring species richness using traditional methods can be challenging. Environmental DNA (eDNA) metabarcoding, which uses high-throughput sequencing and universal primers to amplify DNA from multiple species present in an environmental sample, has shown great promise for augmenting results from traditional sampling to characterize fish communities in aquatic systems. Few studies, however, have compared exhaustive traditional sampling with eDNA metabarcoding of corresponding water samples at a small spatial scale. We intensively sampled Boardman Lake (1.4 km<sup>2</sup>) in Michigan, USA from May to June in 2019 using gill and fyke nets and paired each net set with lake water samples collected in triplicate. We analyzed water samples using eDNA metabarcoding with 12S and 16S fish-specific primers and compared estimates of fish diversity among methods. In total, we set 60 nets and analyzed 180 1 L lake water samples. We captured a total of 12 fish species in our traditional gear and detected 40 taxa in the eDNA water samples, which included all the species observed in nets. The 12S and 16S assays detected a comparable number of taxa, but taxonomic resolution varied between the two genes. In our traditional gear, there was a clear difference in the species selectivity between the two net types, and there were several species commonly detected in the eDNA samples that were not captured in nets. Finally, we detected spatial heterogeneity in fish community composition across relatively small scales in Boardman Lake with eDNA metabarcoding, but not with traditional sampling. Our results demonstrated that eDNA metabarcoding was substantially more efficient than traditional gear for estimating community composition, highlighting the utility of eDNA metabarcoding for assessing species diversity and informing management and conservation.</p>
mosartwmpy sample input data; 1980 - 1985
<p>Sample input data spanning the years 1980-1985 for running the mosartwmpy water routing and management model: https://github.com/IMMM-SFA/mosartwmpy</p>
Sample of seismic waveform data for rfmpy tutorial
<p>Sample of seismic waveform data for rfmpy tutorial (<a href="https://github.com/kemichai/rfmpy">https://github.com/kemichai/rfmpy</a>). Sub-set of seismic data from EASI seismic network that are cut around a number of different teleseismic events. The continuous full waveform data from EASI seismic network are available at: the European Integrated Data Archive EIDA; <a href="http://www.orfeus-eu.org/data/eida/">http://www.orfeus-eu.org/data/eida/</a> with the network code <strong>XT</strong>.</p>
Demographic data, disease characteristics, and laboratory findings for Association between polymorphisms within gene coding for tumor necrosis factor (TNF)-alpha with outcomes of treatment in sample of Iraqi patients with Ankylosing Spondylitis taking Etanercept
<p>Demographic data, disease characteristics, and laboratory findings for Association between polymorphisms within gene coding for tumor necrosis factor (TNF)-alpha with outcomes of treatment in sample of Iraqi patients with Ankylosing Spondylitis taking Etanercept</p>
Behavior and diet data collected from i) GPS video camera collars and ii) fecal samples collected from individuals from the Fortymile Caribou Herd
<p>Summer diets are crucial for large herbivores in the subarctic and are affected by weather, harassment from insects and a variety of environmental changes linked to climate. Yet understanding foraging behavior and diet of large herbivores is challenging in the subarctic because of their remote ranges. <a name="_Hlk82429015">We used GPS video-camera collars to observe behaviors and summer diets of the migratory Fortymile Caribou Herd (<i>Rangifer tarandus granti</i>) across Alaska, USA and the Yukon, Canada.</a> First, we characterized caribou behavior. Second, we tested if videos could be used to quantify changes in the probability of eating events. Third, we estimated summer diets at the finest taxonomic resolution possible through videos. Finally, we compared summer diet estimates from video collars to microhistological analysis of fecal pellets. We classified 18,134 videos from 30 female caribou over two summers (2018 – 2019). Caribou behaviors included eating (mean = 43.5%), ruminating (25.6%), travelling (14.0%), stationary awake (11.3%) and napping (5.1%). Eating was restricted by insect harassment. We classified forage(s) consumed in 5,549 videos where diet composition (monthly) highlighted a strong tradeoff between lichens and shrubs; shrubs dominated diets in June and July when lichen use declined. We identified 63 species, 70 genus and 33 family groups of summer forages from videos. After adjusting for digestibility, monthly estimates of diet composition were strongly correlated at the scale of the forage functional type (i.e., forage groups comprised of forbs, graminoids, mosses, shrubs, and lichens; <i>r = </i>0.79, <i>p</i> < 0.01). Using video collars, we identified i) a pronounced tradeoff in summer foraging between lichens and shrubs and ii) the costs of insect harassment on eating. Understanding caribou foraging ecology is needed to plan for their long-term conservation across the circumpolar north and video collars can provide a powerful approach across remote regions.</p>
Replication data for: Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty
<p>Simulated host-parasite communities in Llopis‐Belenguer, C., J. A. Balbuena, I. Blasco‐Costa, A. Karvonen, V. Sarabeev, and J. Jokela. 2022. Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty. Ecology</p> <ul> <li>Full communities</li> </ul> <p>01_full_communities.RDS</p> <ul> <li>Resampled communities affected by host sampling completeness. From 90% to 10% of host sampling completeness every 10% steps</li> </ul> <p>02_resampled_sampling_completeness_90.RDS, </p> <p>03_resampled_sampling_completeness_80.RDS, </p> <p>04_resampled_sampling_completeness_70.RDS, </p> <p>05_resampled_sampling_completeness_60.RDS, </p> <p>06_resampled_sampling_completeness_50.RDS, </p> <p>07_resampled_sampling_completeness_40.RDS, </p> <p>08_resampled_sampling_completeness_30.RDS, </p> <p>09_resampled_sampling_completeness_20.RDS, </p> <p>10_resampled_sampling_completeness_10.RDS</p> <ul> <li>Resampled communities affected by parasite taxonomic resolution at all levels of host sampling completeness. From 90% to 10% of parasite taxonomic resolution and from 100% to 10% of host sampling completeness every 10% steps</li> </ul> <p>11_resampled_taxonomic_resolution_90_sampling_completeness_100-10.RDS, </p> <p>12_resampled_taxonomic_resolution_80_sampling_completeness_100-10.RDS, </p> <p>13_resampled_taxonomic_resolution_70_sampling_completeness_100-10.RDS, </p> <p>14_resampled_taxonomic_resolution_60_sampling_completeness_100-10.RDS, </p> <p>15_resampled_taxonomic_resolution_50_sampling_completeness_100-10.RDS, </p> <p>16_resampled_taxonomic_resolution_40_sampling_completeness_100-10.RDS, </p> <p>17_resampled_taxonomic_resolution_30_sampling_completeness_100-10.RDS, </p> <p>18_resampled_taxonomic_resolution_20_sampling_completeness_100-10.RDS, </p> <p>19_resampled_taxonomic_resolution_10_sampling_completeness_100-10.RDS</p> <p> </p>
Pre-processed radial wind: sample data
<p>Sample data for testing and developing wind retrievals. Each file contains one hour of pre-processed (merged) WindCube observations.</p>
Sample DisdroDB Data (for test purposes)
<p>Test</p>
Natrolite - Sample 2 NanED Round Robin, Data: ESR8 & ESR9
<p><em><strong>Natrolite</strong></em></p> <p>The following submission contains the data collection and processing for the sample natrolite under the NanEd round-robin project. Precession Electron Diffraction (PED) was used to collect the dataset on the target crystal. The dataset was processed with PETS2 and eADT software. The table below summarizes the data collection parameters for the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR8 & ESR9 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR2</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR2-S2_ PRAHA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR2_Cry2</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>FEI TECNAI F30 STWIN</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>300 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0197 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Nanodiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>200nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Semi-parallel beam</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>US4000 - CCD camera GATAN (16-bit) (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>15 µm x 15 µm</p> </td> </tr> <tr> <td> <p>Camera Length / Effective Camera Length</p> </td> <td> <p>750 mm / 702 mm</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p>0.00152 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> Hardware Binning</p> </td> <td> <p>2 </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Natrolite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>10</sub>·2H<sub>2</sub>O</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Natural sample from Marianska Skala, Usti nad Labem, Czechia</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>The crystals were grinded in an agata mortar and part of the resulting powder was loaded on the carbon side of a carbon-coated copper grid (300 mesh).</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>131</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-65° to +65°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>250 ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Gatan Digital Micrograph software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 and eADT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Laura Gemmrich Hernández (ESR8) & Marco Santucci (ESR9)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>img: Folder containing images of the diffraction pattern from each frame.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit_unsigned</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Epidote - Sample 1 NanED Round Robin, Data: ESR8 & ESR9
<p><em><strong>Epidote</strong></em></p> <p>The following submission contains the data collection and processing for the sample epidote under the NanEd round-robin project. Precession Electron Diffraction (PED) was used to collect the dataset on the target crystal. The dataset was processed with PETS2 and eADT software. The table below summarizes the data collection parameters for the dataset.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p> </p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p> </p> </td> <td> <p>ESR8 & ESR9 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p> </p> </td> <td> <p>RR1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p> </p> </td> <td> <p>RR1-S1_PISA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p> </p> </td> <td> <p>RR_Cry4</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p> </p> </td> <td> <p>FEI TECNAI F30 STWIN</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p> </p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p> </p> </td> <td> <p>300 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p> </p> </td> <td> <p>0.0197 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p> </p> </td> <td> <p>Nanodiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p> </p> </td> <td> <p>200nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p> </p> </td> <td> <p>Semi-parallel beam</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p> </p> </td> <td> <p>US4000 - CCD camera GATAN (16-bit) (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p> </p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p> </p> </td> <td> <p>15 µm x 15 µm</p> </td> </tr> <tr> <td> <p>Camera Length / Effective Camera Length</p> </td> <td> <p> </p> </td> <td> <p>1000 mm / 1200 mm</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p> </p> </td> <td> <p>0.00126 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> Hardware Binning</p> </td> <td> <p> </p> </td> <td> <p>2 </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p> </p> </td> <td> <p>Epidote</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p> </p> </td> <td> <p>Ca<sub>2</sub>Fe<sub>x</sub>Al<sub>3-x</sub>Si<sub>3</sub>O<sub>13</sub>H</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p> </p> </td> <td> <p>Natural source from Val d'Ossola, Italy</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p> </p> </td> <td> <p>Grinded in an Agatha mortar and suspended in 1ml of EtOH. 4 µL of the suspension were dropped with a pipette on the carbon side of a carbon-coated copper grid (300 mesh).</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p> </p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p> </p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p> </p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p> </p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p> </p> </td> <td> <p>141</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p> </p> </td> <td> <p>-70° to +70°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p> </p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p> </p> </td> <td> <p>2 s</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p> </p> </td> <td> <p>Gatan Digital Micrograph software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p> </p> </td> <td> <p>PETS2 and eADT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p> </p> </td> <td> <p>Laura Gemmrich Hernández (ESR8) & Marco Santucci (ESR9)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p> </p> </td> <td> <p>img: Folder containing images of the diffraction pattern from each frame.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p> </p> </td> <td> <p>tiff_16bit_unsigned</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.