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29,897 results for “activism”
TWristAR - wristband activity recognition
<p>TWristAR is a small three subject dataset recorded using an <a href="https://www.empatica.com/research/e4/">e4 wristband</a>. Each subject performed six scripted activities: upstairs/downstairs, walk/jog, and sit/stand. Each activity except stairs was performed for one minute a total of three times alternating between the pairs. Subjects 1 & 2 also completed a walking sequence of approximately 10 minutes. The dataset contains motion (accelerometer) data, temperature, electrodermal activity, and heart rate data. The .csv file associated with each datafile contains timing and labeling information and was built using the provided Excel files.</p> <p>Each two activity session was recorded using a downward facing action camera. This video was used to generate the labels and is provided to investigate any data anomalies, especially for the free-form long walk. For privacy reasons only the sub1_stairs video contains audio. </p> <p>The Jupyter notebook processes the acceleration data and performs hold-one-subject out evaluation of a 1D-CNN. Example results from a run performed on a google colab GPU instance (w/o GPU the training time increases to about 90 seconds per pass):</p> <table align="center"> <caption>Hold-one-subject-out results</caption> <thead> <tr> <th scope="col">Train Sub</th> <th scope="col">Test Sub</th> <th scope="col">Accuracy</th> <th scope="col">Training Time (HH:MM:SS)</th> </tr> </thead> <tbody> <tr> <td>[1,2]</td> <td>[3]</td> <td>0.757</td> <td>00:00:12</td> </tr> <tr> <td>[2,3]</td> <td>[1]</td> <td>0.849</td> <td>00:00:14</td> </tr> <tr> <td>[1,3]</td> <td>[2]</td> <td>0.800</td> <td>00:00:11</td> </tr> </tbody> </table> <p>This notebook can also be <a href="https://colab.research.google.com/drive/1oD2Hwz0wmyWZqj07RHslUoRmjg_N7jXo?usp=sharing">run in colab here</a>. This video describes the processing <a href="https://mediaflo.txstate.edu/Watch/e4_data_processing">https://mediaflo.txstate.edu/Watch/e4_data_processing</a>.</p> <p>We hope you find this a useful dataset with end-to-end code. We have several papers in process and would appreciate your citation of the dataset if you use it in your work.</p>
Data set - Practical teacher-training program on STEAM activity planning
<p>Data set of answers of 14 Brazilian teachers who took part in a practical teacher-training program on STEAM activity planning. </p>
Dataset for "The Dependence of Solar Flare Magnitude on Sunspot Area During Activity Cycle 24"
<p>Dataset for of Will, Avallone, & Sun (2022), RNAAS, 6, 37 "The Dependence of Solar Flare Magnitude on Sunspot Area During Activity Cycle 24". </p> <p>This is a .cvs file containing the information of 412 solar active regions, including their NOAA numbers, sunspot area, sunspot classification, and the peak GOES soft X-ray flux of the largest flare it produced. The sunspot area data are measured using continuum images from SDO/HMI.</p>
Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall
<p>These are the data and code for the article 'Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall.' Please cite this article when using these data or code.</p>
Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment
<p>Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental and psychiatric disorders. MIA-induced deficits in adolescent and adult offspring have been well characterized, however, less is known about the effects of MIA-exposure on embryo development. To address this gap, we collected high-resolution ex vivo magnetic resonance imaging (MRI) of C57BL/6 mouse embryos (gestational day [GD]18) who were prenatally exposed to MIA either early (GD9) or late (GD17) in gestation. We further examined hippocampal neuroanatomy using electron microscopy and identified differential effects due to MIA-timing. </p> <p>The data published here was collected and analyzed for the following publication available as a preprint on BioRxiv (https://www.biorxiv.org/content/10.1101/2021.07.14.452084v2). Briefly, We identify striking neuroanatomical changes in the embryo brain, particularly in the late exposed offspring. An increase in apoptotic cell density was observed in the GD9 exposed offspring, while an increase in the density of dark neurons and glia, putative markers for increased neuroinflammation and oxidative stress, was observed in GD17 exposed offspring, particularly in females. Overall, our findings integrate imaging techniques across different scales to identify differential impact of MIA-timing on the earliest stages of neurodevelopment.</p> <p>In this dataset, you will find a total of <strong>187 preprocessed structural MRIs </strong>(in MINC format) of whole embryos at gestational day 18. These embryos were exposed to poly I:C or vehicle control (0.9% sterile saline) at GD9 or 17. A multi-channel 7.0-T MRI scanner with a 40 cm diameter bore (Varian Inc., Palo Alto, CA), with a custom-built 16-coil solenoid array was used to acquire T2-weighted, gadolinium enhanced structural images at 40 μm3 resolution images from 16 samples concurrently (3D fast spin echo sequence using a cylindrical k-space acquisition; TR/TE=350/12 ms, echo train length=6, two averages, field-of-view 20 mm x 20 mm x 25 mm, matrix size=504 x 504 x 630).</p> <p>An N4 correction for B1 bias field inhomogeneities and denoising using non-local means (minc_anlm) was applied to the T2-weighted images, and the background was set to zero using minc tools. The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>The dorsal hippocampus was selected as a region of interest in which the total number and density of total cells, dark neurons, dark glia, apoptotic cells were assessed (as presented in our manuscript). This is available in the <strong>EM_raw_data_per_slice.csv</strong> and <strong>EM_average_per_mouse.csv </strong>files. </p> <p>Included in this data set are the <strong>structural MRIs in MINC format</strong>, corresponding demographics information (<strong>demographics.csv</strong>), electron microscopy data from the dorsal hippocampus (<strong>EM_raw_data_per_slice.csv</strong> and <strong>EM_average_per_mouse.csv)</strong> data, and a <strong>readme.txt</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles.The raw (not-preprocessed) MINC files, as well as MINC files cropped to the head of the embryos available upon request to the authors. </p> <p>Finally, the authors would like to acknowledge the funding bodies that supported the completion of this work including the Canadian Institute for Health Research, the Fonds de Recherche du Québec en Santé, and the Healthy Brains for Healthy Lives at McGill University.</p>
A corpus-based study of the acquisition of the English progressive by L1 Chinese learners: From prototypical activities to marked statives
<p>This article investigates how EFL learners’ progressive markings are influenced by the lexical aspect of verbs, modality (spoken vs. written), and proficiency levels, focusing on the controversial issue of stative verbs in progressives in L2 acquisition. Spoken (SECCL) and written (WECCL) corpus data from two proficiency levels of Chinese EFL learners and comparison data from native English speakers (COCA) were analyzed. The results suggest that in both learner and native data the progressive -<em>ing</em> is strongly associated with activity verbs, stative verbs being least likely to be inflected with the progressive<em>,</em> as predicted by the Aspect Hypothesis (Andersen and Shirai 1994, 1996). However, inconsistently with the Aspect Hypothesis, this association strengthens with higher proficiency levels. Learners’ use of stative verbs in the progressive and the overextended use of stative progressives was also found to be related to spoken vs. written mode of production and proficiency levels, with learners retreating from overextension as their proficiency increases. A usage-based account of the findings is proposed.</p>
The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes
<p>Ecological infrastructures (EI), defined as natural or semi-natural structural elements, are important to support biodiversity and could play a crucial role in counteracting the well-known impacts of intensive agriculture. Yet, the importance of EI remains largely unexplored in Mediterranean agricultural landscapes and for species providing essential ecosystem services such as bats. Here, we evaluated the role of different EI types – in terms of location (riparian vs terrestrial) and vegetation physiognomy (woody vs non-woody) – in shaping bat guild activity in crop fields located in the floodplains of the Iberian Peninsula. We recorded 60,732 bat sequences in 96 crop fields and characterized 106 EI patches via an adaptation of the Biodiversity Potential Index (BPI). We found that the activity of mid-range echolocators (MRE) and long-range echolocators (LRE) was twofold higher when the nearest EI patch was riparian (i.e., contiguous to a watercourse) than when it was terrestrial. When assessing changes in bat activity in crop fields in relation to a gradient distance from EI types, our results revealed both distinct and similar effects of the location and vegetation physiognomy of the EI on bat guilds. For instance, while only the LRE guild positively responded to the proximity of woody EI, both MRE and LRE showed a marked increase of activity when increasing distances to non-woody EI, thus suggesting low bat activity levels near these features. Our habitat quality assessment also revealed that woody EI and riparian EI had higher biodiversity potential and related habitat quality, thus contributing to our understanding of bat responses to EI type in crop fields. As riparian areas are rarely targeted in biodiversity-friendly measures in farmland, we strongly recommend including riparian EI (especially the woody type) in conservation planning as they are crucial for both biodiversity conservation and ecosystem functioning.</p>
Fig. 3 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 3. – Holotype of Cyphia stheno Webb at P. [Silva s.n., P00088662; © Muséum national d'Histoire naturelle, Paris]
Data sets for assessing potential CC impacts on the activity of the Vögelsberg landslide
<p>Bias-corrected air temperature and precipitation time series (RCM sample from EURO CORDEX) for Kleinvolderberg station near the Vögelsberg landslide (OAL-AT) under RCP8.5, monthly water balance components derived from an empirical model for six elevation steps under current and potential land cover conditions for 1950-2100, median monthly displacement and current hydrological forcing of the Vögelsberg landslide</p>
A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence
<p>Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.</p> <p> </p> <ul> <li>BSPM_filtered.dat <ul> <li>Contains the filtered ECG Data</li> </ul> </li> <li>BSPM_original.bdf <ul> <li>Contains the originally recorded signal.<br> Information on the file format itself can be found here: <a href="https://www.biosemi.com/faq/file_format.htm">https://www.biosemi.com/faq/file_format.htm</a><br> Links to various toolboxes to open the file can be found here: <a href="https://www.biosemi.com/download.htm">https://www.biosemi.com/download.htm</a></li> </ul> </li> <li>CT_DataDCM.zip <ul> <li>Contains the recorded CT images of heart and torso in DCM file format</li> </ul> </li> <li>ECG_NodeIndices.txt <ul> <li>Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest</li> </ul> </li> <li>Endocardial_Surface_Papillary.stl <ul> <li>Segmented endocardial surface including papillary muscles</li> </ul> </li> <li>Mat_LeadField.dat <ul> <li>Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order</li> <li> <pre><code class="language-python"># Python example of usage # Define a read_vm_vec function which reads your calculated data beforehand import numpy as np t_begin = 0 t_end = 400 LF_mat = np.loadtxt('Mat_LeadField.dat') times = np.linspace(t_begin, t_end, t_end-t_begin) result = np.zeros((len(times), 31)) for i,t in enumerate(times): vm_vec = read_vm_vec(t) result[i, :] = LF_mat.dot(vm_vec)[0:31] result = np.insert(result, 0, times, axis=1) np.savetxt('BSPM.dat', result)</code></pre> <p> </p> </li> </ul> </li> <li>Mesh_PurkinjeTree.vtp <ul> <li>The resulting optimized Purkinje Node tree. We recommend using <a href="https://www.paraview.org/">ParaView</a> for visualization</li> </ul> </li> <li>Mesh_StimPoints.vtp <ul> <li>The resulting points of stimulation.</li> </ul> </li> <li>Stim_IndexTime.dat <ul> <li>Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time</li> </ul> </li> <li>Mesh_Ven.vtu <ul> <li>Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.</li> <li>Material:<br> Right Ventricle 30<br> Left Ventricle 31</li> </ul> </li> <li>Mesh_Torso.vtu <ul> <li>Contains the whole torso mesh.</li> <li>Material:<br> Fat 2<br> Bones 3<br> Blood 9<br> Cartilage 14<br> Liver 20<br> Lungs 17<br> Right Ventricle 30<br> Left Ventricle 31<br> Right Atrium 32<br> Left Atrium 33<br> Aorta 60<br> Pulmonary artery 61<br> Left Vena Jugularis 62<br> Right Vena Jugularis 62<br> Post Vena Cava 62</li> </ul> </li> </ul> <p> </p>
Dataset for "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies"
<p>Dataset relating to the publication "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies".</p> <p>Raw input and output data as well as pre- and postprocessing scripts and figures.</p>
Research dat for: "Active Tectonics of the North Tunisian Continental Margin"
<p>The data are the figures in high-resolution for the article: "Active Tectonics of the North Tunisian Continental Margin"</p>
Small mammal ARTS: Orion receiver data for site radiomapping and vole tracking, and scripts and results for localization and activity estimates
<p class="FirstParagraph">This data set accompanies "An Automated Radio-Telemetry System (ARTS) for Monitoring Small Mammals". </p> <p class="FirstParagraph">The behavior of small fossorial mammals, such as voles, is extremely difficult to observe in natural environments. Small mammals were traditionally studied with labor intensive methods such as trapping and recapture or radio telemetry via homing, which require week/months of work and produce static home range estimates.</p> <p class="FirstParagraph">In pursuit of better understanding natural history and behavioral ecology we implemented an automated radio telemetry system (ARTS) to continuously monitor small mammals. We used an isotropic antenna array coupled with broadband receivers to estimate animal positions with nonlinear least squares, nonparameteric, and Bayesian trilateration methods. We then used Lomb-Scargle periodograms to estimate activity patterns of freely-behaving Prairie voles.</p>
Physiological signals during activities for daily life: Dataset
<p>The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I.</p> <p> </p> <table align="center"> <thead> <tr> <th scope="col"> </th> <th scope="col">Participant 1</th> <th scope="col">Participant 2</th> <th scope="col">Participant 3</th> <th scope="col">Participant 4</th> </tr> </thead> <tbody> <tr> <td>Age</td> <td>67</td> <td>55</td> <td>60</td> <td>63</td> </tr> <tr> <td>Gender</td> <td>Male</td> <td>Female</td> <td>Male</td> <td>Female</td> </tr> <tr> <td> <p>Final Valid Days</p> </td> <td>9</td> <td>15</td> <td>12</td> <td>13</td> </tr> <tr> <td>Total EMAs</td> <td>42</td> <td>57</td> <td>64</td> <td>46</td> </tr> </tbody> </table> <p> Table I. Summary of participants' collected data.</p> <p> </p> <p>This dataset provides three different type of labels. <em>Activeness</em> and <em>happiness</em> are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between <em>0</em> and <em>4</em>.<br> These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment.</p> <p>The data we provide in this repository consist of two type of files:</p> <ul> <li><strong>CSV files</strong>: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints. <br> </li> <li><strong>Excel files</strong>: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for <em>pleasure</em>, <em>activeness</em> and <em>mood</em> can be found in this file. </li> </ul> <p><strong>NOTE: </strong>Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files.</p> <p> </p> <p>Code and a tutorial of how to labelled and extract features can be found in this repository: <a href="https://github.com/edugm94/temporal-feat-emotion-prediction">https://github.com/edugm94/temporal-feat-emotion-prediction</a></p> <p> </p> <p>References:</p> <p>[1] . A. Russell, “A circumplex model of affect,” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980</p>
Longitudinal structural MRI, MRS, and behavioral data for mice prenatally exposed to maternal immune activation at gestational day 9
<p>Previous evidence from our lab (https://cobralab.ca/) and others suggest that prenatal exposure to maternal immune activation (MIA) can impact trajectories of neurodevelopment as measured through brain anatomy and behavior in mice. Yet, there are still open questions regarding the alterations to developmental trajectories, as well as the impact on brain chemistry, that this data set seeks to explore. The dataset presented here includes magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) data from two timepoints, adolescence (postnatal day [PND 35]) and young adulthood (PND 60) in C57BL/6J mice prenatally exposed either to poly I:C (POL) inducing maternal immune activation (MIA) or saline (SAL) at gestational day (GD) 9. The dataset also includes three behaviors acquired after each scanning session with 2 days of rest between the scans and each behavior: open field test, social novel object preference test, and prepule inhibition. Finally, the data also include cytokine assays acquired from a separate sample of pregnant mice and a test-retest of MRS acquired from a voxel in the anterior cingulate area. </p> <p>The data here published were collected and analyzed for a paper under review, available as a preprint where more details can be found here: https://www.preprints.org/manuscript/202203.0136/v1. In brief, using whole-brain, voxelwise analysis techniques (deformation-based morphometry) we found MIA subtly altered developmental trajectories, reducing volume relative to SAL offspring in the hippocampus and the anterior, right caudate putamen, and increasing volume in the posterior, left caudate putamen and cerebellum. Additionally, there was a trending decrease of myo-inositol and GABA in MIA offspring at PND 60 compared to SAL controls. Finally, there was a trending decrease in ratio of distance travelled in the anxiogenic center zone of an open field compared to the outer areas at PND 35 for MIA offspring. </p> <p>In this dataset you will find a total of <strong>80 preprocessed structural MRIs</strong> in minc format acquired at postnatal day ~35 and ~60 in mice exposed to 5mg/kg poly I:C or vehicle control (0.9% sterile saline) at GD9. The images are included in CUPO_MIA_mncs.zip. These are T1-weighted structural images with two averages; repetition time (TR)/echo time (TE) = 21.55 ms/5.13 ms, matrix size = 260 x 158 x 210, voxel dimensions =&thinsp;70 µm isotropic, flip angle =&thinsp;20°, 23 min total using 5% isoflurane for induction, 1.5% for maintenance of anesthesia during the scan on a cryogenically-cooled surface coil. T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the <strong>demographics.csv</strong> file. </p> <p>Behavioural tests were performed following the postnatal day 35 and 60 scans in all animals with a 2 day rest period. These include: open field test, three chambered social approach, and prepulse inhibition. The data for all of these tests is presented in individual .csv spreadsheet and includes data for both the timepoints evaluated. Additionally, cytokine panels were collected from an independent cohort of 7 dams. <strong>MRS </strong>data are included in two formats: 1) preprocessed quantifications from LCModel software in csvs, and 2) raw data with press and press_w (respectively water supressed and unsupressed acquisitions) for analysis. The raw data were released in upload version 1.1.0. MRS was acquired from a 1.2 x 2.6 x 2.5 mm3 voxel in the ACA with a Point Resolved Spectroscopy sequence (PRESS; TR/TE=3000/8.5 ms, 256 averages). Within the raw_data.zip,</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, the MRS data (csvs and raw files), and a <strong>README</strong> file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </p>
Overview of PASTA Questionnaires: Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study
<p>Overview of questionnaires used in the PASTA project (Physical Activity through Sustainable Transport Approaches (PASTA))</p> <p>English version</p>
Common Dynamic Determinants Govern Quorum Quenching Activity in N-terminal Serine Hydrolases
<ul> <li> <p>(File-01) Free enzymes molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ enzymes</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from minimization, equilibration and production runs</p> </li> <li> <p>output files from minimization, equilibration and production runs</p> </li> <li> <p>raw data for analysis and visualization:</p> <ol> <li> <p>protein backbone RMSD evolution</p> </li> <li> <p>binding cavity dynamics analysis</p> </li> <li> <p>principal component analysis of catalytic machinery (with states' representatives in PDF format)</p> </li> </ol> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-02) Ligand-enzyme complexes molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from minimization, equilibration and production runs</p> </li> <li> <p>output files from minimization, equilibration and production runs</p> </li> <li> <p>raw data for analysis and visualization:</p> <ol> <li> <p>protein backbone RMSD evolution</p> </li> <li> <p>near-attack-conformation (NAC) stabilization</p> </li> <li> <p>HSLs RMSD evolution</p> </li> <li> <p>MM/PBSA binding energy estimation</p> </li> <li> <p>HSLs heavy atoms RMSF</p> </li> </ol> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-03) Michaelis complex ensemble generation molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from ensemble generation production runs</p> </li> <li> <p>output files from ensemble generation production runs</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(Files-04-06) Ligand-enzyme QM/MM steered molecular dynamics:</p> <ul> <li> <p>input parameters for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>ensemble of input restart files generated in stage 3</p> </li> <li> <p>general input files for QM/MM steered MD simulations in AMBER</p> </li> <li> <p>output restart files from QM/MM MD equilibration simulations and QM/MM steered MD simulations</p> </li> <li> <p>output files and output work from QM/MM steered MD simulations</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-07) Ligand-enzyme QM/MM steered molecular dynamics data for analysis and visualization:</p> <ul> <li> <p>reaction states ensembles (in PDB format) extracted from QM/MM steered MD simulations with crucial distances measured</p> </li> <li> <p>evolution of the reaction coordinate elements in the first and second step of acylation</p> </li> <li> <p>representative states of the reaction stages for visualization (in PDB format)</p> </li> <li> <p>different dynamics of the residues gating access to acyl-binding cavity at TS1</p> </li> <li> <p>different dynamics of the residues gating overall access to active site at TS2a</p> </li> <li> <p>different system-dependent bending of the HSLs at TS1 and TS2a</p> </li> </ul> </li> </ul> <p> </p>
Fig. 3 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 3. The activity pattern of the otter in the three protected areas, based on the number of otter recordings at the observation sites during March 2011–April 2016.
Fig. 6 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 6. Seasonal activity patterns of Lutra lutra in the study area during the study period based on the number of otter crossings through the observation sites.
Estimation of groundwater flow rate by an actively heated fiber optics based thermal response test in a grouted borehole
<p>The dataset contains the numerical data and the <em>in-situ</em> measurements in the manuscript titled "Estimation of groundwater flow rate by an actively heated fiber-optics-based thermal response test in a grouted borehole". The data is stored in MAT files, which are Binary MATLAB files. There are a series of codes used in this manuscript to estimate groundwater flow rates. The codes were written in MATLAB Live Script, version 2021b.</p> <ul> <li>The numerical data contains temperatures of the heating stage in different thermal response tests in a numerical model, which considers the borehole effects. The model is set up by COMSOL Multiphysics, and a series of flow rates is set to the model respectively for different thermal response tests.</li> <li>The <em>in-situ</em> measurements include temperatures of the heating stage in an actively heated fiber-optics-based thermal response test, which was performed in July 2021 in the grouted borehole, which is located in the lower section of the Sima bend of the Yangtze River. The temperature for the flow rate estimation was thinned to 120 s records from 10s records for the limited computing resources.</li> <li>The <em>data_process.mlx</em> provides pre-processing for the observational data recorded by Silixa Ultima-M MK2 DTS. The <em>estimation_process.mlx </em>gives a groundwater flow estimation case in a grouted borehole.</li> </ul>
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.