Find research datasets worth reusing

opencc-by-4.0Mar 2018View details →

WV-TTL: water vapor mixing ratio from GEOSCCM and trajectory model simulations in tropical tropopause layer

This dataset includes 100 hPa water vapor mixing ratio simulated from a trajectory transport model and a climate-chemistry model in the tropical tropopause layer from 2005 to 2016 in the format of netCDF. The data are monthly and have three dimensions as lon/lat/time in the unit of parts per million by volume. Also included the tropical average time series of indices for Brewer-Bobson circulation (BDC), tropospheric temperature and/or Quasi-biennial Oscillation (QBO) from ERAi/MERRA-2/GEOSCCM. These indices are used in a multivariate regression.

opencc-by-4.0Sep 2018View details →

Data and Code For PhD Thesis- Making an Impression: An Assessment of the Role of Print Surfaces Within the Technological, Commercial, Intellectual and Cultural Trajectory of Book Illustration c.1780-c.1860.

Overview These datasets support research found in the PhD Thesis entitled: Making an Impression: An Assessment of the Role of Print Surfaces Within the Technological, Commercial, Intellectual and Cultural Trajectory of Book Illustration c.1780-c.1860, submitted by William Finley. The data was provided by the British Library as part of a wider ambition to digitise millions of book illustrations (further details can be found here: <a href="https://github.com/BL-Labs/imagedirectory">https://github.com/BL-Labs/imagedirectory</a>). The main dataset contains 6063 rows and 108,784 illustrations. All of the data is held in comma-separated values (csv) files. The collection has been subdivided in order to interrogate the dataset further. All of the datasets have been interrogated using Rscript (R 3.4.2 El Capitan Build), the codes of which have been included in the dataset repository.  Guidance to Datasets 'Counts of Illustration by Size over Time' <ul> <li>Title: Title of Book</li> <li>Author: Author of Book </li> <li>Pub_Place: Location the book was published</li> <li>Book_Id: British Library image identifier </li> <li>YEAR: Year the book was published</li> <li>Image_Count: Number of illustrations belonging to that book</li> <li>N: Number of illustrations according to the size of illustration</li> <li>IMAGE_TYPE: Size of Illustration </li> </ul> 'Density Graphs of the Position of Illustrations on the Page' <ul> <li>X1: The X position in pixels of the top left of the image</li> <li>Y1: The Y position in pixels of the top left of the image</li> <li>X2: Width in pixels of the Image</li> <li>Y2: Height in pixels of the Image</li> <li>PERCENT_PAGE: Percentage of the page taken up by illustrations</li> <li>TYPE: Percentage Range of the Page taken up by illustrations </li> <li>VOL: Volume</li> </ul> 'Frequency of Illustrations Across First 100 Pages of the Book 1800-1850' <ul> <li>Page: Page number </li> <li>X1: The X position in pixels of the top left of the image</li> <li>Y1: The Y position in pixels of the top left of the image</li> <li>X2: Width in pixels of the image</li> <li>Y2: Height in pixels of the image</li> <li>PERCENTPAGE: Percentage of the page occupied by illustration</li> <li>SIZE: Size category each illustration belongs to</li> </ul> 'Illustration Arrangement in Single Books and Editions' <ul> <li>Book_ID: British Library image identifier</li> <li>Page_NO: Page Number Illustration is found on </li> <li>X: The X position in pixels of the top left of the image</li> <li>Y: The Y position in pixels of the top left of the image </li> <li>WIDTH: Width in pixels of the image</li> <li>HEIGHT: Height in pixels of the image</li> <li>IMAGE_SIZE: Percentage of the page occupied by illustration</li> </ul> 'Relative Frequency of Printing Methods Over Time' <ul> <li>Publisher: Publisher of the book</li> <li>Title: Title of the book</li> <li>first_author: Author</li> <li>pub_place: Location the book was published</li> <li>book_identifier: British Library image identifier</li> <li>Year: Year the book was published </li> <li>COUNT_IMAGE: Number of illustrations found in a given book</li> <li>YEAR_BOOK_COUNT: Number of book published in a given year</li> <li>YEAR_IMAGE: Number of Illustrations found in books published in a given year</li> <li>AVERAGE_IMAGE: Average number of illustrations found in books published in a given year</li> <li>IMAGE_METHOD: Print method used to produce the illustration</li> <li>IMAGE_SIZE_COUNT: Number of illustrations printed in books in a given year according to its size</li> <li>IMAGE_SIZE: Categories of image sizes</li> </ul> Contact Will Finley can be contacted via the following email. Information listed is accurate at the time of publication Email: wafinley1@sheffield.ac.uk

opencc-by-sa-4.0Oct 2018View details →

Single-cell -omics datasets containing a trajectory

Contains datasets to compare trajectory inference methods. See <a href="https://benchmark.dynverse.org">https://benchmark.dynverse.org</a> These files can be opened in R using `readRDS` . Each file is a list containing at least: <ul> <li>expression and counts: filtered log2 normalised expression files and raw counts</li> <li>milestone_network, milestone_percentages and divergence_regions: the trajectory model</li> </ul>

opencc-by-nc-nd-4.0Nov 2018View details →

Mocap gait motion samples - Optical marker trajectories

======================= Summary ======================= This database gathers a set of optical marker-based motion capture (mocap) tracking samples. Each sample corresponds to a single acquisition containing the 3D trajectories of a set of markers along a continuous interval of time. Keywords: Motion capture, mocap, Gait analysis, marker tracking. ======================= Data description ======================= Several persons wearing small reflective balls (markers) were asked to walk normally while recorded using motion capture cameras. These cameras, previously calibrated, are thought to detect the 2D pixel image position of the markers against the background thanks to IR lightning they are provided with. The XYZ position can be afterwards recovered by means of photogrammetric methods, whereas the tracking is kept across the frames. This data set contains the raw 3D trajectories of the aforementioned markers along the time. The set of markers is the layout proposed by Kadaba, Ramakrishnan, and Wootten, from the Helen Hayes Hospital. More details can be found in 'Dynamics of Human Gait' from Vaughan et al. Check out 'HH_marker_arrangement.png' for a visual description on marker placement. This database was built on purpose to serve as ground truth for the doctoral research "Cloud Point Labeling in Optical Motion Capture Systems", written by Juan L. Jiménez B. and supervised by Prof. Manuel Graña, from the Computational Intelligence Group, University of the Basque Country (UPV/EHU), and expected to be presented by the beginning of 2019. This work has been partially supported by the EC through project CybSPEED funded by the H2020 MSCA-RISE grant agreement Num. 777720. ======================= Equipment ======================= The following elements were taken to harvest the data:   - Six (6) motion capture cameras model S250e from Natural Point Inc. ( https://optitrack.com/ );   - Fifteen (15) passive-reflective optical markers;   - One computer running motion capture software from s.t.t.;   - A number of other minor accessories as camera wall mounts, Ethernet wires, adhesive tape, coffee and time. ======================= Technical details =======================   Database size: 71 recordings between 1.5 and 7 seconds long, summing up more than 20.000 frames   Exercise: regular gait motion at different paces   People: 14 different persons of different ages and shapes were recorded. No gait pathologies openly acknowledged.   Number of markers: 15 (Helen Hayes marker system)   Native camera resolution: 800x800 pixels   Sampling frequency: 100Hz   Cartesian units: millimeters   Right handed reference frame, +Y vertical upwards, ground height at Y=~0 ======================= Data base format =======================   The database is composed by 71 different files, one per gait sequence. Each file is written in plain ASCII, comma separated values (CSV) format. Each line represents an instant of time (frame) containing the 3D Cartesian positions of all the 15 markers with to 2 mantissa digits and dot symbol as decimal separator. A triple 'nan nan nan' instead of actual numbers stands for marker occlusion and consequently no measured position can be given. The coordinates are numerically arranged as follows:       X1; Y1; Z1; X2; Y2; Z2; ... Xi; Yi; Zi; ... X15; Y15; Z15;        ... where 'i' refers to the i-th marker among the following list:    1- Right ASIS    2- Left ASIS    3- Sacrum    4- Right Femoral wand    5- Left Femoral wand    6- Right Femoral epicondyle    7- Left Femoral epicondyle    8- Right Tibial wand    9- Left Tibial wand   10- Right Malleolus   11- Left Malleolus   12- Right Heel   13- Left Heel   14- Right Metatarsal head II   15- Left Metatarsal head II ======================= Privacy statement ======================= The personal privacy of the volunteers, adults of 25 and over, remains protected since no confidential data has been collected at any time. The authors count on the express written consent of the company to use the gathered data in the scope of academic research and as ground truth for the aforementioned PhD Thesis, including its publication under the specified license terms. ======================= Further information ======================= If you find any mistake or experience any trouble in the interpretation of the data, please do not hesitate to contact us.

opencc-by-4.0Mar 2019View details →

Micro-CT scans, whole-test meshes, and internal chamber segments of planktonic foraminifera for three-dimensional analysis of inter- and intra-specific variation in ontogenetic growth trajectories

Here, we release tomographic reconstructions of 42 planktonic foraminifera from plankton tows and sediment traps, along with meshes and shrinkwrap meshes the whole tests and internal meshes of segmented chambers. Shrinkwrap meshes are test meshes that have been modified to close all pores and apertures in the test. Additionally, we have provided sample metadata for each specimen and volumetric measurements for the tests and chambers. This dataset was used in a study of ontogenetic growth in planktonic foraminifera and its variation within and among species.  The CT-scans and reconstructions were obtained at Naturalis Biodiversity Center in Leiden, the Netherlands with a Zeiss Xradia 520 Versa micro-CT scanner. The meshes and segments were created at Yale University. <ol> <li>Sample_Metadata.csv: Spreadsheet containing information on the sampling localities and dates for all specimens.</li> <li>Scan_data.csv: Spreadsheet containing metadata for all micro-CT scans including current strength, pixel size, voltage, image height, image width, and the number of images taken.</li> <li>Whole_Test_Measurements.csv:  Spreadsheet containing measurements of linear dimensions (axis1, axis2, axis 3), total number of chambers, calcite test volume, calcite test surface area, shrinkwrap volumes, and and shrinkwrap surface areas for all specimens.</li> <li>Chamber_Measurements.csv: Spreadsheet containing measurements of individual internal chamber segments, including position from the final chamber (F-chamber), position from the first chamber (Chamber), volume, and surface area.</li> <li>CT_Scan_Stacks.zip: reconstructed micro-CT image stacks (.tif files) for each specimen.</li> <li>Meshes.zip: Meshes of the test calcite, the shrinkwrap, and the internal chamber segments for each specimen (.stl 3D mesh files). Regular test meshes are named with the format “SampleID.stl”, and shrinkwrap meshes are named “SampleID-WRAP.stl”. Chamber meshes are named “SampleID-CH#.stl” and “SampleID-CH#-Wrap.stl”. Chambers are numbered in relation to their position from the final chamber, with “CH1” being the final chamber and “CH2” being the penultimate chamber.</li> </ol> This data is described and analyzed in the manuscript “Three-Dimensional Analysis of Inter- and Intraspecific Variation in Ontogenetic Growth Trajectories of Planktonic Foraminifera” submitted to the journal Marine Micropaleontology.

opencc-by-4.0Jul 2019View details →

Robot trajectory data for "Biohybrid Fly-Robot Interface system performs active collision avoidance"

This dataset includes the videos of the trajectories of the biohybrid robot (Fly-Robot Interface), performing collision avoidance at the patterned wall corners (90 and 60 degrees). A python script for the manual tracking is also attached, as well as the processed coordinates of each individual robot trajectory, where two tracking markers were chosen on the front-left and front-right corners of the robot. Serial Number <20: the videos at 90-degree wall corner Serial Number >20: the videos at 60-degree wall corner

opencc-by-4.0Sep 2019View details →

An evaluation of compression algorithms applied to moving object trajectories

This file contains the dataset, source code and results presented in the paper entitled "An evaluation of compression algorithms applied to moving object trajectories" published in the International Journal of Geographical Information Science in 2019. Abstract: The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.

FIGURE 4 in Talpa fossilis or Talpa europaea? Using geometric morphometrics and allometric trajectories of humeral moles remains from Hungary to answer a taxonomic debate

FIGURE 4. Boxplot of the centroid sizes. Bottom and top of the boxes are the first and third quartiles; horizontal solid black lines represent the median; whiskers represent the minimum and maximum values.

FIGURE 3. 1 in Talpa fossilis or Talpa europaea? Using geometric morphometrics and allometric trajectories of humeral moles remains from Hungary to answer a taxonomic debate

FIGURE 3. 1, Scatterplot of the first two axes of the PCA. Deformation grids refer to axes extremes (positive and negative values). 2, Scatterplot of the first and third axes of PCA. Deformation grids refer to axes extremes (positive and negative values).

FIGURE 5. 1 in Talpa fossilis or Talpa europaea? Using geometric morphometrics and allometric trajectories of humeral moles remains from Hungary to answer a taxonomic debate

FIGURE 5. 1, CCA scatterplot of the shape and size variables. 2, Plot of the Euclidean distances between the predicted shape values of Talpa fossilis and T. europaea against 10 discrete CS intervals.

FIGURE 2. 1 in Talpa fossilis or Talpa europaea? Using geometric morphometrics and allometric trajectories of humeral moles remains from Hungary to answer a taxonomic debate

FIGURE 2. 1, Landmarks (large grey circles) and semilandmarks (small white circles) digitized on the humerus in caudal norm: 1) lateral end of greater tuberosity; 2) articular facet for clavicula; 3) proximal edge of the articular facet for clavicula; 4) bicipital notch; 5) proximal end of lesser tuberosity; 6) medial edge of the minor tuberosity; 7) lateral edge of the lesser tuberosity; 8) bicipital ridge; 9) middle point of the bicipital tunnel; 10) lateral end of the scalopine ridge; 11) proximal end of the teres tubercle; 12-14) surface of the teres tubercle; 15) distal end of the teres tubercle; 16-18) minor sulcus; 19) posterior margin of the lateral epicondyle; 21-22) lateral epicondyle; 22-24) trochlear area; 25-27) medial epicondyle; 28) posterior margin of the medial epicondyle; 29-32) greater sulcus; 33-36) humeral head. Scalebar equals 1 mm. 2, Insertion areas of the main muscles involved in the digging movement. 1. Pectoral ridge where muscle Pectoralis pars sternalis inserts. 2. Teres tubercle where muscles Teres major and Latissimus dorsi inserts.

Coarse-grained methanol trajectory

This dataset is a supplement to the paper "Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks," available at https://arxiv.org/abs/2406.12112.  To create this dataset, an all-atom trajectory of liquid methanol was generated using the GROMOS 54A7 force field and the LAMMPS software package.  The coarse-grained mapping described in the aforementioned paper was applied to the trajectory; the resulting positions, velocities, and forces are provided here. Additionally, the periodic cell is reported, as well as values of the radial distribution function calculated using the coarse-grained positions. For completeness, mass and species arrays are also included. The original all-atom trajectory was generated using a Nosé-Hoover thermostat at 700 K. After equilibration, 50,000 timesteps of 1 fs were computed and every 500th frame recorded. The resulting 100 frames were used to generate this dataset. The data is stored in a single Numpy .npz file, which contains eight arrays:  <table> <tbody> <tr> <td>key</td> <td>shape</td> <td>size (bytes)</td> <td>data units</td> </tr> <tr> <td>cells</td> <td>(100, 3, 3)</td> <td>7200</td> <td>Å</td> </tr> <tr> <td>forces</td> <td>(100, 1024, 3)</td> <td>2457600</td> <td>kcal/mol/Å</td> </tr> <tr> <td>masses</td> <td>(100, 1024)</td> <td>819200</td> <td>amu</td> </tr> <tr> <td>positions</td> <td>(100, 1024, 3)</td> <td>2457600</td> <td>Å</td> </tr> <tr> <td>rdf_bins</td> <td>(300,)</td> <td>2400</td> <td>Å</td> </tr> <tr> <td>rdf_values</td> <td>(300,)</td> <td>2400</td> <td>unitless</td> </tr> <tr> <td>species</td> <td>(100, 1024)</td> <td>819200</td> <td>unitless</td> </tr> <tr> <td>velocities</td> <td>(100, 1024, 3)</td> <td>2457600</td> <td>Å/ps</td> </tr> </tbody> </table>   Total size of file: 9.03 MB

Synthetic Aircraft Trajectory Dataset

This dataset comprises synthetically generated aircraft trajectories for multiple specific airport pairs across Europe. Generated using advanced machine learning techniques, the dataset includes high-resolution spatial and temporal information for each trajectory. It features key flight parameters such as latitude, longitude, altitude, and time, along with synthetic identifiers for each flight. This dataset is ideal for air traffic management research, flight path analysis, and the development of predictive models in aviation.

Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon

We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories. The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.

TRACMASS trajectory data traced from the Tsushima Strait

We used high-resolution GLORYS12V1 reanalysis data from the E.U. Copernicus Marine Service as input for the TRACMASS model to obtain the trajectory data.

opencc-by-4.0Jul 2024View details →

Molecular Dynamics Trajectories Exploring the Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

<h3>Primary data for the publication "Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution" by de Bruyn, Dorn, Rossetti, Fernandez, Outeiro, Schulz and Carloni. Submitted to the Journal of Chemical Information and Modeling.</h3> Included are all GROMACS input files, parameterised topologies, starting and final configurations, and trajectories for the lowest temperature replica (at 300 K, lowest of 32 replicas between 300-500 K exchanging according to the REST2 algorithm (Wang et al. 2011)). The data is split into three archives: <ol> <li>all_atom_trajectories.zip <ul> <li>contains all input files and all atom trajectories including solvent</li> <li>trajectories written at 100 ps intervals</li> </ul> </li> <li>protein+ion_trajectories.zip <ul> <li>contains configuration/non-parameterised topologies and trajectories excluding solvent, but including ions</li> <li>trajectories written at 10 ps intervals</li> </ul> </li> <li>additional_simulations.zip <ul> <li>contains the all atom trajectories and input files, and</li> <li>solvent-free trajectories above,</li> <li>for the additional simulations in the Supplemental Information of the article: <ul> <li>includes the DES-Amber-based simulations with 64 replicas between 300-600 K, and</li> <li>a99SB-disp-based simulations</li> </ul> </li> </ul> </li> </ol>   Folders are named according to the following top level scheme: <ul> <li>DES-Amber simulations/ Simulations created using the DES-Amber force field (Tucker et al. 2022)</li> <li>a99SB-disp simulations/ SImulations created using the a99SB-disp force field for Intrinsically Disordered Proteins (IDPs) (Robustelli et al. 2018)</li> </ul> Sub-folders follow the following scheme: <ul> <li>AS/ Simulations of the physiological form of wild-type Human α-Synuclein <ul> <li>unphosphorylated</li> </ul> </li> <li>pAS/ Simulations of the physiological form of wild-type Human α-Synuclein <ul> <li>phosphorylated at S129</li> <li>with double negative charge</li> </ul> </li> <li>pASH/ Simulations of the physiological form of wild-type Human α-Synuclein (a99SB-disp simulations only) <ul> <li>phosphorylated at S129</li> <li>with a single negative charge (monoprotonated)</li> </ul> </li> </ul>

opencc-by-4.0Sep 2023View details →

All-atom Gromacs Trajectory of POPC/TOCL bilayer mixture

All-atom bilayer mixture of POPC/TOCL 1:1 simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 540 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 100 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K). POPC is 16:0,18:1 PC; TOCL is tetraoleoyl cardiolipin

opencc-by-4.0Sep 2023View details →

All-atom Gromacs Trajectory of POPC/POPE/TOCL bilayer mixture

All-atom bilayer mixture of POPC/POPE//TOCL 50/25/25 mol% simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 570 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 75 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K). POPC is 16:0,18:1 PC; POPE is 16:0,18:1 PE; TOCL is tetraoleoyl cardiolipin

opencc-by-4.0Sep 2023View details →

All-atom Gromacs Trajectory of POPE/TOCL bilayer mixture

All-atom bilayer mixture of POPE/TOCL 1:1 simulated in an NPT ensemble with Gromacs and the CHARMM36 force field from Castillo et al, 2022, Mol. Pharmaceutics. 19:1839-1852 (<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00926">https://doi.org/10.1021/acs.molpharmaceut.1c00926</a>). The trajectory represents 530 ns with frames output every 20 ps. The bilayer has 120 lipids total (60 lipids per leaflet) and is hydrated with 100 waters/lipid and sodium ions to neutralize the system. The simulation was done at 37C (310.15K). POPE is 16:0,18:1 PE; TOCL is tetraoleoyl cardiolipin