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1,176 results for “Acquisition”
SBC LTER: Meteorological and sea surface data from the R/V Pt. Sur Underway Data Acquisition System (UDAS) in the Santa Barbara Channel: LTER15, 2006-02-02 to 2006-02-09
The data described here were collected on LTER15 which took place from 2006-02-02 to 2006-02-09 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 2 basic types of measurements: Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous atmospheric climate measurements. Underway acoustic doppler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
SBC LTER: Meteorological and sea surface data from the R/V Pt. Sur Underway Data Acquisition System (UDAS) in the Santa Barbara Channel: LTER16, 2006-04-26 to 2006-05-03
The data described here were collected on LTER16 which took place from 2006-04-26 to 2006-05-03 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 2 basic types of measurements: Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous atmospheric climate measurements. Underway acoustic doppler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
2m resolution DEM based stereoscopy Pleiades acquisitions in Telangana, South-India
<p>Four steroscopy pairs of Pléiades images (Pléiades © CNES 2021 Distribution AIRBUS DS) were acquired over Telangana state in 01 and 14 of June 2016 and the 16 of June 2019 and licensed to CESBIO by Airbus. The Pléiades Digital Elevation Model is a derivative product subject to the CC-BY-NC 4.0 license preventing commercial use. The four .tif files correspond to these DEM at 2m resolution. The dates were selected because the Rainwater Harvesting System small reservoirs of the areas covered were empty. Elevations within small reservoirs are thus equivalent to a bathymetry. We verified that 2 meter resolution is very adapted to retrieve the geometries of narrow and steep dikes that dam each small reservoirs.</p>
Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)
<p>This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4</p> <p>This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included.</p> <p>Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946</p> <p><br> Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2022.</p>
Test Data from a Study on Latin Vocabulary Acquisition (Cicero)
<p>The dataset contains test results from an intervention study with intermediate learners in two high schools in Berlin. In total, 58 students participated in three groups (= classes). The intervention materials and tests are published as well.</p> <p>The study was the first to collect empirical data on what German students actually know about Latin vocabulary and how they handle their vocabulary knowledge. One of the main goals of the research project is to establish a broad understanding of vocabulary knowledge in Latin lessons in Germany, which aims at a versatile education of (cross-linguistically helpful) vocabulary competence.</p>
Test Data from a Study on Latin Vocabulary Acquisition (Ovid)
<p>The dataset contains test results from an intervention study with intermediate learners in two high schools in Berlin (2018-2019). In total, 60 students participated in three groups (= classes). The intervention materials and tests are published as well.</p> <p>A key question of the still ongoing research project is: How can vocabulary competence in a historical language such as Latin be acquired and deepened by using corpus-based, i.e. context-based, methods? This question is based on a broad understanding of vocabulary that refers back to theories of the mental lexicon.</p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Effects of intolerance of uncertainty on subjective and psychophysiological measures during threat acquisition and delayed threat extinction
<p>This dataset includes measurements of intolerance of uncertainty (Intolerance of Uncertainty Scale [Freeston et al., 1994]), trait anxiety (State-Trait Anxiety Inventory [Spielberger et al., 1983]), skin conductance response (SCR), fear potentiated startle (FPS) and fear ratings (RAT) acquired in a differential fear conditioning paradigm with habituation and threat acquisition training on one day and extinction training, mood induction (by presenting negative vs. neutral slides), re-extinction training, reinstatement and reinstatement-test 24h later. Overall, 66 participants (female = 44, aged between 18 and 40 years, M = 25.76, SD = 5.82) took part in the study. Several participants had to be excluded due to technical issues (n = 3), non-responding (SCR: n = 2; auditory startle blink: n = 1) and no SCRs to the CSs (n = 1). Visual CSs were two shapes resembling snowflakes. The US consisted of a train of three 2 ms electrotactile square-waves (inter stimulus interval, ISI: 50 ms) and was delivered 7.9 s after each CS+ onset (100% reinforcement rate) during threat acquisition training and three times during reinstatement. The duration of the ITIs ranged from 10 to 13 s (M = 11.5). For SCR measurements, a 1 Hz lowpass filter and a gain of 5 or 10 μΩ were applied. SCR data were scored by using the semi-automatic scoring system Autonomate (Green et al., 2014), down sampled to 10 Hz and scored as the first response within 0.9 to 4 s after CS onset as SCR from trough to peak with a maximum rise time of 5 s. SCRs were square root transformed to reduce skew and z-scored within individuals across trials for day 1 and day 2 separately. To elicit the auditory startle blink, a 95 dB white noise burst was presented simultaneously on both ears. Startle probes were administered 6 or 8 s after the ITI-onset and 6 or 7 s after CS-onset. A gain of 5000 at 1000 Hz and a band-pass filter (28–500 Hz) were applied. Data were rectified and integrated online (averaged over 20 samples) and scored semi-automatically by using a custom-made computer program (EDA View, developed by Prof. Dr. Matthias Gamer, University of Würzburg) as trough to peak 20–120 ms after startle probe onset. For analyses, FPS data was z-scored within individuals across trials for day 1 and day 2 separately. To acquire fear ratings, participants rated throughout the experiment, how much stress, fear, and tension they experienced, when they last saw the CSs. Answers had to be logged in via button press within 7 s on a visual analog scale (VAS) ranging from zero (answer = none) to 100 (answer = maximum). Unlogged ratings were considered as missing values.</p>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
Videos during training and acquisition of awake Sheep MRI
<p>These videos are provided in support of Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> . One illustrates our technique to train sheep to lie down, while the other shows the acquisition of a T1-weighted image from an awake and unrestrained sheep.</p> <p>This Version 2 also includes the file Sheepvoice-V3.mp4, which contains footage of several training steps. </p>
Data underpinning "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences."
<p>=================================================<br> Robert Casper Brand, PhD Candidate<br> Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.<br> =================================================</p> <p>This folder contains the images and datasets used to generate the figures of the paper named: "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences." </p> <p>Each figure of the paper, with its corresponding data, is contained in an opensource TikZ format file. The TikZ files include both information on the axis as well as the supporting data and can be opened with any generic text editor. For more information on TikZ, see:<br> https://www.sharelatex.com/learn/TikZ_package). </p> <p>Where datasets were too large to be run by standard TeX distributions, the data was attached in an alternative format, and a TikZ wrapper included.</p> <p>The figures can be generated through any of the opensource TeX distributions. For more information on LaTeX and TeX, please see: <br> https://www.latex-project.org/get/ and<br> https://www.sharelatex.com/learn/Pgfplots_package.</p> <p>A compilation example of all figures, which also lists any additional packages, is included in the "wrapper. Tex" file. The output of this process was added to this folder as well (wrapper.pdf).</p> <p>The included files were created using directly from Matlab using the matlab2tikz code:<br> https://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz</p>
The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance
<p>We present the open design of the PIRATE, an anthropometric earPlug with exchangable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance. Its outer shape is available in 5 sizes and provides a deep, tight and reproducible fit in virtually all human ears. The design includes a recess to accommodate a MEMS microphone. Thus, the same microphone can be conveniently used in different earplugs without losing accuracy, and the microphone can be removed for calibration. The PIRATE or previous versions of it have been utilized in several studies with more than 200 subjects</p> <p>From the provided model, the earplugs can be 3D printed, and only minor working steps are necessary before use. These steps are described in the documentation.</p> <p> </p> <p>Reference:</p> <p>Denk F., Brinkmann F., Stirnemann S., Kollmeier B. (2019) "The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance," Fortschritte der Akustik - DAGA, Rostock, Germany</p>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
Dataset to the manuscript "Deciduous tundra shrubs shift toward more acquisitive light absorption strategy under climate change treatments"
<p>The three datasets were used for the statistical analysis for the manuscript entitled "Deciduous tundra shrubs shift toward more acquisitive light absorption strategy under climate change treatments".</p>
Master and Landsat-8 simultaneous acquisition datacubes for the quantification of directional anisotropy in Thermal Infra-Red domain
<p>‎</p> <p>This dataset contains datacubes of simultaneous Landsat-8 and Master<sup><a href="#fn.1">1</a></sup> data as listed in table <a href="#org4c9ba67">1</a>. Those pairs have been identified by cross-searching Landsat-8 and Master archive for Master flight tracks with a Landsat-8 overpass during the flight. The dataset has been collected and analysed in the following paper:</p> <p><em>Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. 2023. <a href="https://hal.science/hal-04073733">⟨hal-04073733⟩</a></em></p> <table> <caption>Table 1: List of valid Master and Landsat-8 pairs</caption> <thead> <tr> <th scope="col"><strong>Id</strong></th> <th scope="col"><strong>Master track id</strong></th> <th scope="col"><strong>Landsat L2 product id</strong></th> </tr> </thead> <tbody> <tr> <td>1</td> <td><code>2013-03-29_18:06:53</code></td> <td><code>LC08_L2SP_038037_20130329_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>2</td> <td><code>2013-04-11_18:14:46</code></td> <td><code>LC08_L2SP_041036_20130411_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>3a</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040036_20130522_20200913_02_T1</code></td> </tr> <tr> <td>3b</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040037_20130522_20200913_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>4</td> <td><code>2013-12-05_18:23:35</code></td> <td><code>LC08_L2SP_043035_20131205_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>5a</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039035_20140331_20200911_02_T1</code></td> </tr> <tr> <td>5b</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039036_20140331_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>6a</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041036_20140414_20200911_02_T1</code></td> </tr> <tr> <td>6b</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041037_20140414_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>7</td> <td><code>2014-04-28_18:22:43</code></td> <td><code>LC08_L2SP_043035_20140428_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>8a</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044033_20140606_20200911_02_T1</code></td> </tr> <tr> <td>8b</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044034_20140606_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>9a</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043034_20141021_20200910_02_T1</code></td> </tr> <tr> <td>9b</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043035_20141021_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>10a</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040036_20150528_20200909_02_T1</code></td> </tr> <tr> <td>10b</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040037_20150528_20200909_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>11</td> <td><code>2018-06-19_18:28:30</code></td> <td><code>LC08_L2SP_042034_20180619_20200831_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>12a</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043033_20210330_20210409_02_T1</code></td> </tr> <tr> <td>12b</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043034_20210330_20210409_02_T1</code></td> </tr> </tbody> </table> <p>Variables of interest are resampled on a common UTM grid at 100m. The resulting datacubes are distributed as netCDF files, and contains the variables listed in table <a href="#org09b0cd2">2</a>. Landsat-8 pixels flagged as cloud and missing pixels are set to NaN.</p> <table> <caption>Table 2: Description of variables in netCDF files</caption> <thead> <tr> <th scope="col"><strong>Variable Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td><code>ls8_lst</code></td> <td>Landsat-8 Land Surface Temperature (K)</td> </tr> <tr> <td><code>ls8_bt</code></td> <td>Landsat-8 Surface Brightness temperature (K)</td> </tr> <tr> <td><code>ls8_b2</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b3</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b4</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b5</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_emis</code></td> <td>Landsat-8 emissivity (unitless)</td> </tr> <tr> <td><code>ls8_water</code></td> <td>Landsat-8 water mask (1 = water, 0 = no water)</td> </tr> <tr> <td><code>ls8_snow</code></td> <td>Landsat-8 snow mask (1 = snow, 0 = no snow)</td> </tr> <tr> <td><code>ls8_view_zenith</code></td> <td>Landsat-8 view zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_view_azimuth</code></td> <td>Landsat-8 view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>ls8_sun_zenith</code></td> <td>Landsat-8 sun zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_sun_azimuth</code></td> <td>Landsat-8 sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> <tbody> <tr> <td><code>master_lst</code></td> <td>Master Land Surface Temperature (K)</td> </tr> <tr> <td><code>master_bt</code></td> <td>Master Surface Brightness Temperature (K)</td> </tr> <tr> <td><code>master_emis3</code></td> <td>Master B47 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis4</code></td> <td>Master B48 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis</code></td> <td>Master interpolated emissivity (unitless)</td> </tr> <tr> <td><code>master_view_zenith</code></td> <td>Master view zenith angle (degrees)</td> </tr> <tr> <td><code>master_view_azimuth</code></td> <td>Master view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>master_sun_zenith</code></td> <td>Master sun zenith angle (degrees)</td> </tr> <tr> <td><code>master_sun_azimuth</code></td> <td>Master sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> </table> <p>Landsat-8 products were downloaded from the collection 2 level 2 archive from the EarthExplorer portal<sup><a href="#fn.2">2</a></sup>. Master L1B products, containing radiances and viewing angles, as well as L2 products, containing LST and geo-location grids, were requested on the Master website<sup><a href="#fn.1">1</a></sup>. Landsat-8 viewing angles have been computed by using a C program publicly available on USGS website<sup><a href="#fn.3">3</a></sup>.</p> <p>Footnotes:</p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://masterprojects.jpl.nasa.gov/">https://masterprojects.jpl.nasa.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file">https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file</a>, consulted on 2022.09.12</p>
Datasets for "Placebo effects of transcranial direct current stimulation on motor skill acquisition"
<p>The following two .csv files contain the participant level data for the primary analyses conducted within the research study:</p> <p>"Placebo effects of transcranial direct current stimulation on motor skill acquisition"</p> <p>Data are formatted in long format for ease of analysis</p> <p>Dataset used in first analysis - Estimation of TDCS effect and Placebo effect including a NO TDCS control group</p> <p>ALLGROUPS.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS</p> <p>Dataset used in second analysis - Estimation of expectancy effects on Performance among TDCS groups ONLY</p> <p>TDCSGroupsONLY.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS<br> PostExp = Expectancy score post practice<br> PreExp = Expectancy score pre practice<br> Suggestibility = Suggestibility score<br> Prior Know = Prior knowledge of TDCS (Yes/No)<br> Prior Study = Participation in a study using TDCS (Yes/No)</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Horizontal acquisition of Symbiodiniaceae in the Anemonia viridis genetic data
<p>All metazoans are in fact holobionts, resulting from the association of several organisms, and organismal adaptation is then due to the composite response of this association to the environment. Deciphering the mechanisms of symbiont acquisition in a holobiont is therefore essential to understanding the extent of its adaptive capacities. In cnidarians, some species acquire their photosynthetic symbionts directly from their parents (vertical transmission) but may also acquire symbionts from the environment (horizontal acquisition) at the adult stage. The Mediterranean snakelocks sea anemone, <i>Anemonia viridis </i>(Forskål, 1775), passes down symbionts from one generation to the next by vertical transmission, but the capacity for such horizontal acquisition is still unexplored. To unravel the flexibility of the association between the different host lineages identified in <i>A. viridis </i>and its Symbiodiniaceae, we genotyped both the animal hosts and their symbiont communities in members of host clones in five different locations in the North Western Mediterranean Sea. The composition of within-host symbiont populations was more dependent on the geographical origin of the hosts than their membership to a given lineage or even to a given clone. Additionally, similarities in host symbiont communities were greater among genets (<i>i.e.</i> among different clones) than among ramets (<i>i.e. </i>among members of the same given clonal genotype). Taken together, our results demonstrate that <i>A. viridis</i> may form associations with a range of symbiotic dinoflagellates and suggest a capacity for horizontal acquisition. A mixed-mode transmission strategy in <i>A. viridis</i>, as we posit here, may help explain the large phenotypic plasticity that characterises this anemone.</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.