Find research datasets worth reusing

Data set for "A Magnesium Binding Site And The Anomeric Effect Regulate The Abiotic Redox Chemistry Of Nicotinamide Nucleotides"

Data associated with Sebastianelli L, Kaur H, Chen Z, Krishnamurthy R, Mansy SS (2024) A magnesium binding site and the anomeric effect regulate the abiotic redox chemistry of nicotinamide nucleotides. Chem Eur J. 30, e202400411. DOI: 10.1002/chem.202400411 [<a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/chem.202400411">link</a>]

Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" II: Cascade Volcanic Arc

Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Cascades used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Lesser Antilles used in the same manuscript (doi:10.5281/zenodo.10912445). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator).

opencc-by-4.0May 2024View details →

DeltaCAN: A new data set of Canadian Arctic and subarctic coastal deltas

Arctic coasts constitute the critical interface between land and sea, and are subject to rapid changes caused by a warming climate. Current trends throughout the Arctic show increasing erosion trends, while other parts of the coast are experiencing prograding trends. Until now, a vast majority of our knowledge of Arctic coastal evolution is confined to site-specific studies with limited geospatial representation. Here, we present DeltaCAN, a novel data set on the locations of Canadian deltas larger than 500 m in width derived by visual interpretation of freely available satellite imagery. DeltaCAN is Canada's first nationwide coastal detection covering 250.000 km of coastline in the Arctic, identifying 2712 deltas. The inventory is based on inspection of remotely-sensed satellite imageries, developed through an expert-based mapping approach where we implemented a quality control mechanism to assess the completeness of the data set. The DeltaCAN data set allows for assessing changes at an unprecedented spatial extent, improving our understanding of delta morphodynamics.

dryad40/100

Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?

Deep learning has emerged as a robust tool for automating feature extraction from 3D images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artificial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared to the external structure, due to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from 3D images.

opencc-zeroMay 2024View details →

Movement data set for trust assessment (Drapebot robot cell/Profactor)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)

This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>) Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019.  Number of sample sites: 500 Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos.  Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a>

Movement data set for trust assessment (Drapebot robot cell/Dallara)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task in a near-production setting from 5 participants all familiar with carbon-fibre draping. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 5 files for 5 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Movement data set for trust assessment (Drapebot robot cell/DLR)

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016). For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training. The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores). Items for Trust perception scale - HRI, Schaefer 2016: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016: <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do  </li> <li>The speed at which the gripper picked up and released the components made me uneasy    </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul>   K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218. G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Data Set for the Journal Article "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks"

This data archive contains all data newly created in the following publication: Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja- Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, and Markus Reiher, "Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks", in preparation. The directory contents are as follows: <ul> <li>steered_eschenmoser.tar.xz: Dump of the database created during the steered exploration</li> <li>steered_exploration_protocol_chemoton_3.1.json: Protocol used for the steered exploration</li> </ul>

Data for "Bar to bank height ratio sets bank erosion rate"

CaseA1_Q20Zbar15Zbank35_initial.csv ~ CaseB6_Q30Zbar45Zbank45_last.csv: Survey data of riverbed topography at the beginning of the experiment (_initial.csv) and at the end of the experiment (_last.csv). The first line of each file indicates the number of measurement points, and the second and subsequent lines indicate the x, y, and z coordinates. velocity results.zip: Results of flow velocity analysis using iRIC Nays2DH. The ipro files stored in the zip can be opened by installing the free software "iRIC" (https://i-ric.org/en/). Please refer to the manual and the examples (https://i-ric.org/en/solvers/nays2dh/) for how to view calculation results and calculation conditions. bar height and near bank velocity.xlsx: Table on bar height and near-bank velocity for each case.

The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) Part I: Instrument description and level 1 radiances (data set)

The data set uploaded to this repository is outlined in a manuscript submitted to the journal Atmospheric Measurement Techniques. A. BB_effective_emissivity:                            Holds the data set used to characterise instrument calibration target emissivity B. ILS_data:                                                   Holds the data set used to model the instrument spectral lineshape C. Time_resolved_spectral_response:           Holds the data set establishing the spectral stability of the instrument D. Zenith_radiances_20220323_1100UTC:   Holds calibrated radiances acquired by FINESSE to demonstrate it accuracy and precision. Also included in this file is an LBLRTM simulation using coincident ERA5 profile information for the time and location of the observations The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE). Part I: Instrument description and level 1 radiances Jonathan E. Murray1,2, Laura Warwick3, Helen Brindley1,2, Alan Last1, Patrick Quigley1, Andy Rochester1, Alexander. Dewar1, Daniel. Cummins1 1 Department of Physics, Imperial College London, SW7 2BX, UK 2 National Centre for Earth Observation, UK 3 ESA-ESTEC, Noordwijk, Netherlands In the manuscript Part (I) we describe the FINESSE system configuration, outlining the FINESSE spectral characteristics, the data acquisition methodology and the calibration strategy. As part of the process, we evaluate the stability of the system, including the impact of knowledge of blackbody target emissivity and temperature.  We also establish a numerical description of the instrument line shape.  We demonstrate why it is important to account for these effects by assessing their impact on the overall uncertainty budget on the level 1 radiance products from FINESSE.

Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set

The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities.   The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting.

opencc-by-4.0Mar 2024View details →

Supplementary data to the paper: Toward a Novel Set of Pinna Anthropometric Features for Individualizing Head-Related Transfer Functions

Supplementary research data to the <a href="https://doi.org/10.5281/zenodo.14338958" target="_blank" rel="noopener">paper</a>: <blockquote> Davide Fantini, Stavros Ntalampiras, Giorgio Presti, and Federico Avanzini. Toward a novel set of pinna anthropometric features for individualizing head-related transfer functions. In Proceedings of the 21th Sound and Music Computing Conference, Porto, Portugal, July 2024. </blockquote> The repository includes the research data generated in the abovementioned paper. In particular, the repository includes: <ul> <li><a href="../api/records/10805885/draft/files/README.md/content" target="_blank" rel="noopener noreferrer">README.md</a>: instructions for the data</li> <li><a href="../api/records/10805885/draft/files/pinna_images.mat/content" target="_blank" rel="noopener noreferrer">pinna_images.mat</a>: pinna depth images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/records/10805885/draft/files/landmarks.mat/content" target="_blank" rel="noopener noreferrer">landmarks.mat</a>: coordinates of the landmarks manually annotated on pinna depth images</li> <li><a href="../api/records/10805885/draft/files/anthropometry.mat/content" target="_blank" rel="noopener noreferrer">anthropometry.mat</a>: anthropometric parameters automatically extracted from manually annotated landmarks</li> <li><a href="../api/records/10805885/draft/files/anthropometry_documentation.pdf/content" target="_blank" rel="noopener">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../records/12698286/files/poster.pdf?download=1">poster.pdf</a>: poster presented at the SMC conference 2024</li> </ul> The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>). A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-anthropometry-extraction" target="_blank" rel="noopener">here</a>.

opencc-by-4.0Dec 2023View details →

Protein haplotype sequences obtained by ProHap from the 1000 Genomes Project data set

Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the 1000 Genomes Project, aligned with the GRCh38 genome build (<a href="https://www.internationalgenome.org/data-portal/data-collection/grch38">https://www.internationalgenome.org/data-portal/data-collection/grch38</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts. The complete configuration file for each ProHap run is attached to this repository. This data set contains six compressed directories, five representing the superpopulations included in the 1000 Genomes Project (<a href="https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project">https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project</a>), and one created using all the samples included in the 1000 Genomes data set: <ul> <li>AFR - African</li> <li>AMR - American</li> <li>EUR - European</li> <li>SAS - South Asian</li> <li>EAS - East Asian</li> <li>ALL - all participants in the 1000 Genomes Project</li> </ul> Each of the directories contains the following files: <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap, using alleles with at least 1 % frequency within the selected population</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the simplified fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>. For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>. When using these databases in your publication, please cite: Va&scaron;íček, J., Kuznetsova, K.G., Skiadopoulou, D. et al. ProHap enables human proteomic database generation accounting for population diversity. Nat Methods (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a>

Data Sets ''Proton Pump Inhibitor Omeprazole Alters the Spiking Characteristics of Proteinoids''

Data Sets for the paper ''Proton Pump Inhibitor Omeprazole Alters the Spiking Characteristics of Proteinoids''

Data set (traces) for Software implementations of Multiplication Gadgets: SEAL

This file holds the power-consumption traces in our experiments (using SCALE Board) regarding: "Towards secure software implementations of masking schemes (Multiplication gadgets) in Assembly for ARM Cortex-M3". - Format of traces: TRS - Traces for: SNR (random inputs), t-test

Data set, Model, and Catalog for: Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data

Intrinsic colors of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the stellar intrinsic colors with the atmospheric parameters, Teff , log g, and [M/H], which is an improvement of the widely used blue-edge method. The dust reddening toward each line-of-sight can then be calculated by the observed colors minus the derived intrinsic colors. Here we provide the related data sets: <ul> <li>xgb_model.pkl: the trained XGBoost model.</li> <li>use_xgb.py: a simple script showing how to use the XGBoost model to predict intrinsic colors.</li> <li>data_set.fits: this fits file contains the training and test sets, separated into four data arrays: <ul> <li>X_train: X-data (teff,logg,mh) of the training set.</li> <li>X_test:  X-data (teff,logg,mh) of the test set.</li> <li>y_train: y-data (BP-RP, BP-Ks, J-Ks) of the training set.</li> <li>y_test:  y-data (BP-RP, BP-Ks, J-Ks) of the test set.</li> </ul> </li> <li>IC_catalog.csv: a catalog containing a representative set of intrinsic colors at three bands ('BPRP0', 'BPK0', and 'JK0' in columns) as a function of typical Teff, logg, and [M/H] ('teff', 'logg', and 'mh' in columns).</li> </ul> With the above data and model, users can apply the trained XGBoost to new sources to predict their intrinsic colors and calculate their dust reddenings. One can further control the quality of the prediction by selecting training-like sources with the training set and estimating the <a>generalization error by the test set.</a>

X-ray diffraction data set for PDB 9G3L: LecB from PA01 in complex with beta-fucosylamide-indole derivative

X-ray diffraction images collected on proxima 1 Soleil  the 7th of march 2024 at SOLEIL synchrotron, Saint Aubin, France for PDB ID 9G3L using a DECTRIS EIGER X 16M detector. X-ray dataset and xdsme processing for the structure of LecB from Pseudomonas aeruginosa PA01 strain in complex with synthetic beta-fucosylamide-indole derivative. Images 1-900 were removed during processing and resolution was cut to 1.74 angstrom.

Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"

Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"