Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
982
datasets available to search
ShareScore release 0.7.1
Dataset results
982 results for “Interface”
Data from: A semi-automated approach to classify and map ecological zones across the dune-beach interface
<p>This is the raw data behind the publication: </p> <p><strong>A semi-automated approach to classify and map ecological zones across the dune-beach interface</strong></p> <p><strong>Abstract: </strong>Habitat classification and mapping underpins most conservation and management tools, because habitats are often used as a surrogate for all biodiversity. Some habitat boundaries are easy to delineate; however, sandy shores are ecotones or ecoclines given their dynamic interface between the marine and the terrestrial realms. Although methods for mapping habitats along shorelines have been broadly applied, we aim to test a semi-automated approach to mapping across-shore “sub-environments” in this transition zone at a finer scale. Using an empirical dataset of photographs covering a small area (three across-shore transects from each of two different areas) with a high resolution, we tested seven machine learning algorithms to determine which one had the best classification accuracy, and to identify which environmental variables are the main determinants of classifications. The randomForest, stochastic gradient boosting, and C5.0 algorithms most accurately classified the photographs as the correct sub-environment. Based on the randomForest algorithm, the variables entropy, drift cover rate, local slope, segmented vegetation cover and number of points with sand or marine litter had the highest influence on the classification. There was no sensitivity to spatial variation alongshore. This approach can be used to map sub-environments at larger scales using drone technology to capture georeferenced digital photographs systematically. Consequently, coastal habitats can be mapped at a finer scale without causing disturbance to this especially sensitive ecotone.</p>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Supporting information of a study for the definition and evaluation of a graphical user interface for housing co-design
<p>This dataset is from a study that intends to define, prototype and test a graphical user interface for a housing co-design system. To define the requirements of the interface, we conducted interviews with professionals of architecture, urbanism and social sciences areas, as well as with housing cooperatives and inhabitants of these institutions. An interface solution was prototyped, tested and refined. Then we conducted a heuristic evaluation and a summative evaluation. Such evaluations involved the testing of a high-fidelity prototype, to receive feedback from UX/UI experts, potential users (inhabitants) and architects.</p> <p>S1_File refers to the interview protocol used with the three groups of interviewees. We share the English and Portuguese versions of the interviews with professionals and the original (Portuguese) and translated versions of the remaining ones since these were conducted in Portuguese.</p> <p>S2_File is a dataset reporting the results of the interviews. Each question includes the answers given and the identification (anonymized) of the interviewees who responded to that question.</p> <p>S3_File describes the usability issues identified by the experts during the heuristic evaluation of the high-fidelity prototype. The first page organizes the issues by severity (left) and priority (right). The remaining pages have a table for each issue, including rows for problem designation, heuristic violated, problem description, solution proposal, severity degree, and an image of the interface pointing to the referred issue.</p> <p>S4_File refers to the results of the heuristic evaluation. It includes the identification of each issue, which expert (anonymized) identified such issue, and the heuristic it violates, with the sum of the times each heuristic was violated at the end of each column. At the right, a table presents the consolidation of issues, organized by priority, with columns identifying the issue, severity level, frequency, and priority.</p> <p>S5_File is the script given to potential users to experiment with the interface during the summative evaluation. This script guides the user through the tasks to perform since the prototype does not have all the features functioning.</p> <p>S6_File refers to the questionnaires applied during the summative evaluation with inhabitants. It includes a preliminary questionnaire, a Single Ease Question (SEQ) questionnaire, a System Usability Scale (SUS) questionnaire, and a Graphical User Interface (GUI) questionnaire.</p> <p>S7_File refers to the results of the summative evaluation with inhabitants (potential users).</p> <ul> <li>Page A refers to the preliminary questionnaire with demographic information such as age, gender, education, relationship with digital technologies, etc. Each field corresponds with each inhabitant (anonymised) and the sum and percentage. In the middle, a table presents a summary of the consolidation. In the right possible relations are presented. </li> <li>Page B presents the results of the SEQ questionnaire, identifying the ratings each inhabitant (anonymized) gave each task. A summary of such values is at the right. </li> <li>On page C, the result of each rating for the SUS questionnaire given by each inhabitant (anonymized) is shown. At the bottom is the calculation of the SUS score.</li> <li>Page D presents the GUI questionnaire results for each inhabitant (anonymized), with the average and SD identified for each question. A summary of such results is on the right.</li> <li>Page E holds the notes taken by the researchers based on their observations regarding task performance. The information is organized in tables for each step of each task and includes the completeness, attempts, and time taken for each inhabitant (anonymized) to complete such task. Also, the sum, percentage, average, and SD are registered. Next to each task is a table identifying how many participants accomplished the task at the first attempt.</li> <li>Page F refers to the strong and weak aspects identified by the inhabitants. Strong and weak aspects are identified, as well as which inhabitant (anonymized) has identified them. The sum and percentage are also given. At the right, there is a table with the consolidation of results by combining similar answers. </li> </ul> <p>S8_File refers to the results of the discussion with architects after experiencing the interface. Such results relate to the positive and negative aspects that the architects identified in the interface and its usefulness for architecture. The left table identifies the strong and weak aspects that architects (anonymized) identified and the sum and percentage associated with them. The table on the right consolidates such results, with similar responses combined.</p>
Data and code for "First Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface"
<p>This is the complete release of data and code for publication "First Principles Assessment of ZnTe and CdSe as Prospective Tunnel Barriers at the InAs/Al Interface", <a href="https://chemrxiv.org/engage/chemrxiv/article-details/66ffec1acec5d6c1422377f8" target="_blank" rel="noopener">10.26434/chemrxiv-2024-w17ws-v2</a></p> <p>Contains VASP inputs (POSCAR, KPOINTS, INCAR) and partial data (large VASP output data files not included), post processed numpy files for plotting, python and jupyter scripts for processing and plotting, and workflows. Jupyter scripts that lead to figures in publication are labeled with figure number. Requires python packages of "<a href="https://github.com/caizefeng/vaspvis.git">https://github.com/caizefeng/vaspvis.git</a>" and "https://github.com/DerekDardzinski/OgreInterface" to plot and make structures respectively.</p> <p>Zip files each contain the different sections for the data, with bulk calculations, single material slab calculations, SM-SM bilayers, SM-Al bilayers, ZnTe and CdSe trilayers, interface matching and optimization, and OGRE structure provided.</p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
<p>This dataset contains replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". The dataset consists of pickled Pandas DataFrame files, along with training, validation, and (for DB5-Plus) test filename lists for cross-validation, that can be used to develop and evaluate protein interface prediction models. This dataset also contains the externally generated residue-level PSAIA and HH-suite3 features for users' convenience (e.g. raw MSAs and profile HMMs for each protein complex). Our GitHub repository linked in the "Additional notes" metadata section below provides more details on how we parsed through these files to create our cross-validation datasets. The GitHub repository for DIPS-Plus also includes scripts that can be used to impute missing feature values and convert the final "raw" complexes into DGL-compatible graph objects. Since our final DGL graph representation for each complex uses PyTorch tensors in its construction of residue embeddings, the final representation of each complex can easily be adapted to fit the users' needs (e.g. feeding a complex's 2D residue feature tensors into a convolutional neural network).</p>
COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces
<p>Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science.</p> <p> </p> <p><strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong></p> <p><strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong></p>
Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics
<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years). The cohort was 68% male and 32% female. In terms of education, 16% had attended only high school, while 32% had a bachelor's degree, 42% a master's degree, and 11% a doctorate. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p><strong>Experiment Description:</strong></p> <p>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>—a VR environment that renders virtual arms from a first-person perspective—while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---TASK #<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMO<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHP<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MIMOHPVR<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---ME<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p> </p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)
<p>This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.</p>
Buried Interface Engineering Enables Efficient and 1,960-hour Isos-L-2i Stable Inverted Perovskite Solar Cells
<p>High-performance perovskite solar cells (PSCs) typically require interfacial passivation, yet this is challenging for the buried interface, owing to the dissolution of passivation agents during the deposition of perovskites. Here, we overcome this limitation with in-situ buried interface passivation – achieved via directly adding a cyanoacrylic acid-based molecular additive, namely BT-T, into the perovskite precursor solution. Classical and ab-initio molecular dynamics simulations reveal that BT-T spontaneously may self-assemble at the buried interface during the formation of the perovskite layer on a nickel oxide hole transporting layer. The preferential buried interface passivation results in facilitated hole transfer and suppressed charge recombination. In addition, residual BT-T molecules in the perovskite layer enhance its stability and homogeneity. We report a power-conversion efficiency (PCE) of 23.48% for 1.0 cm2 inverted-structure PSCs. The encapsulated PSC retains 95.4% of its initial PCE following 1,960-hour maximum power point tracking under continuous light illumination at 65°C (i.e., ISOS-L-2I protocol). Our demonstration of operating-stable PSCs under accelerated ageing conditions represents a step closer to the commercialization of this emerging technology.</p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
TOP-100 DOCKING POSES OF FDA APPROVED AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 SPIKE/ACE2 INTERFACE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: Spike Protein/ACE-2, (PDB, 6M0J). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
DS3_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_neurons electrophysiology
<p>Whole-cell current clamp recordings of the electrical activity of neurons plated on P3HT flat and pillars</p>
DS5_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_immunofluorescence
<p>Neuron synaptic expression analysis; neurons and HEK cells morphological analysis</p>
DS4_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_cell viability
<p>cell viability assay on HEK cells and neurons plated on P3HT flat and pillars</p>
Cyclic friction tests of ballast stones interfaces under varying vertical load
<p>This data set contains results from cyclic ballast-ballast friction tests under varying load.<br> Two types of railway ballast were tested: “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> In each friction test, a stone with a distictive tip was sheared over a flat (stone) surface under a given normal load.<br> The applied normal load was increased from 10 N to 100 N in several load steps in each test.<br> For each applied load, a given number of cycles were conducted.<br> In total, 7 tests were conducted for Calcite and 8 for Kieselkalk.<br> The test results are provided in .csv files and contain (among other values) the measured coefficient of friction (CoF) values.<br> The test were organised in three test series. In all conducted test, the angular tip stones were 3D scanned before and after the tests.<br> Test series 1 and 2, involved a 3D scanning before each load increase.<br> The data of these tip scans are also provided (in .ply file format). From the 3D scans the contact areas were calculated (using an alogorithm described in the paper below).<br> A detailed description of the tests and discussion of measurements can be found in:</p> <p>B. Suhr, T.A. Butcher, R. Lewis, K. Six<br> "Friction and wear in railway ballast stone interfaces"<br> Tribology International 2020<br> <a href="https://doi.org/10.1016/j.triboint.2020.106498">https://doi.org/10.1016/j.triboint.2020.106498</a></p> <p><br> This data set is organised as follows:</p> <p>Folder FrictionTests: contains measured data from friction tests in .csv file format.<br> Folder TipScans: contains data from scans of ballast tip stones before/in between/after cyclic friction tests. Meshes are provided in .ply file format.<br> Folder TipScans: contains two files with detail information on the contact area calculation (for the two types of ballast tested)</p> <p>Each folder contains three subfolder: Series1, Series2, Series3, which contains the data of the corresponding test series,<br> see paper above for a more detailed description.</p> <p>Check the README.txt file for more information on the technical aspects</p> <p> </p> <p>The investigated ballast types are the same, as previously investigated</p> <p>1) in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p>2) in shape analysis including 3D scans:<br> Suhr, Bettina, Six, Klaus, Skipper, William A., & Lewis, Roger. (2020).<br> "3D scans of two types of railway ballast including shape analysis information [Data set]".<br> Zenodo. http://doi.org/10.5281/zenodo.3689592</p> <p> </p>
Dataset From: Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes
<p>The dataset for the publication "Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes". DOI: 10.1039/d0sm01257b.</p> <p>Files containing data have .dat extension and are in text format.</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.