Scientific running performance standards and training methodologies

Centre of mass acceleration data was collected while runners ran a long distance overground run. This was for a PhD project investigating running bomechanics related to running performance and runnng injuries.

Centre of mass acceleration data was captured from a tri-axial accelerometer attached to the lower back of runners while they ran for 8 km around an outdoor athletics track.

Kraken2 Arthopod Reference Database v.1Kraken2 (v2.1.2) database containing all 2,593 reference assemblies for Arthropoda available on NCBI as of March 2023.This database was built for and used in the analysis of shotgun sequencing data of bulkDNA from Malaise trap samples collected by the Insect Biome Atlas, in the context of the manuscript "Small Bugs, Big Data: Metagenomics for arthropod biodiversity monitoring" by authors: López Clinton Samantha, Iwaszkiewicz-Eggebrecht Ela, Miraldo Andreia, Goodsell Robert, Webster Mathew T, Ronquist Fredrik, van der Valk Tom (for submission to Ecology and Evolution).For custom database building, Kraken2 requires all headers in reference assembly fasta files to be annotated with "kraken:taxid|XXX" at the end of each header. Where "XXX" is the corresponding National Center for Biotechnology Information (NCBI) taxID of the species. The code used to add the taxID information to each fasta file header, and update the accession2taxid.map file required by Kraken2 for database building, is available in this GitHub repository (also linked under "Related Materials" below).ContentBelow is a list of the files in this item (in addition to the README and MANIFEST files), and their description. The first three files (marked with a ) are required to run Kraken2 classifications using the database. hash.k2d.gz - A hash file with all minimiser to taxon mappings (855 GB).* opts.k2d - A file containing all options used when building the Kraken2 database (64 B).* taxo.k2d - A file containing the taxonomy information used to build the database (385.9 KB).seqid2taxid.map.gz - A file containing contig accession numbers and their corresponding taxids (810.6 MB). Note that this file is needed by Kraken2 when building the database, and as it was updated during custom building, it has been included for reference, but it is not required to use the database for classification.genome_assembly_metadata.tsv - NCBI-generated table (tsv format, gzipped) of all reference assemblies for Arthropoda as of March 2023, which were used in the database construction. This includes columns: Assembly Accession, Assembly Name, Organism Name, Organism Infraspecific Names Breed, Organism Infraspecific Names Strain, Organism Infraspecific Names Cultival, Organism Infraspecific Names Ecotype, Organism Infraspecific Names Isolate, Organism Infraspecific Names Sex, Annotation Name, Assembly Stats Total Sequence Length, Assembly Level, Assembly Submission, and WGS project accession.How to use the databaseDownload the hash.k2d.gz, opts.k2d, and taxo.k2d files to the same directory (e.g. /PATH/TO/DATABASE/).Unzip the hash.k2d.gz file.Install or load Kraken2 to run classification on sequencing data using the database.When running Kraken2, indicate the path to the directory (not the individual files) with the --db flag (e.g. kraken2 --db /PATH/TO/DATABASE/ ...).Note that the whole database must be loaded into memory by Kraken2 to be able to classify any sequencing reads, so ensure you have access to enough memory before running (the uncompressed hash file is around 1.1 TB).We also recommend using the Kraken2 option --memory-mapping, as it ensures the database is loaded once for all samples, instead of once for each individual sample, saving considerable time and resources.For more information on using Kraken2, see the Kraken2 wiki manual.This database was built by Samantha López Clinton (samantha.lopezclinton@nrm) and Tom van der Valk (tom.vandervalk@nrm.se).

Running time of classification on the microarray database.

This dataset has been shared by Nike, encompassing a comprehensive record of all my running activities logged through the Nike Run Club App over three years. This rich dataset includes detailed information on each run, capturing metrics such as distance, pace, and time among others. It reflects my journey of commitment and endurance, punctuated by rigorous training sessions, recovery runs, and personal milestones. A significant highlight of this dataset is its documentation of my dedicated preparation for a major athletic goal—the Paris Marathon 2023. Through this dataset, one can trace my progress, challenges overcome, and the evolution of my running performance over time, offering valuable insights into the discipline and resilience required to train for such a prestigious long-distance running event.

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.

This dataset contains 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet.The data with the athletes' activities are contained in dataframe objects (tabular data) and saved in the Parquet file format using the Pandas library, part of the Python ecosystem for data science. Each Pandas dataframe contains the following data (as different columns) for each athlete (as different rows), the first word identifies the name of the column in the dataframe:- datetime: date of the running activity;- athlete: a computer-generated ID for the athlete (integer);- distance: distance of running (floating-point number, in kilometers);- duration: duration of running (floating-point number, in minutes);- gender: gender (string 'M' of 'F');- age_group: age interval (one of the strings '18 - 34', '35 - 54', or '55 +');- country: country of origin of the athlete (string);- major: marathon(s) and year(s) the athlete ran (comma-separated list of strings).For convenience, we created files with the athletes' activities data sampled at different frequencies: day 'd', week 'w', month 'm', and quarter 'q' (i.e., there are files with the distance and duration of running accumulated at each day, week, month, and quarter) for each year, 2019 and 2020. Accordingly, the files are named 'run_ww_yyyy_f.parquet', where 'yyyy' is '2019' or '2020' and 'f' is 'd', 'w', 'm' or 'q' (without quotes). The dataset also contains data with different government’s stringency indexes for the COVID-19 pandemic. These data are saved as text files and were obtained from https://ourworldindata.org/covid-stringency-index. The Jupyter notebooks that we created and made available in the https://github.com/BMClab/covid19 repository exemplify the use of the data.

This database portrays the surface and shallow subsurface geology of the greater Charleston, S.C. region east of 80°30′ west and south of 33°15′ north. The region covers the entirety of Charleston County and portions of Berkeley, Colleton, Dorchester, and Georgetown Counties. Units locally exposed at the surface range in age from middle Eocene to Holocene, but most of the area is covered by Quaternary interglacial deposits. These are, from oldest to youngest, the Okefenokee, Waccamaw(?), Penholoway, Ladson, Ten Mile Hill, and Wando Formations and the Silver Bluff beds. Two cross sections (not included in the database), one running southeast from Harleyville to the coastline on James Island and the other running along the coastal barrier islands from the town of Edisto Beach to the northeast end of Bull Island at the southwest edge of Bull Bay, portray the complex geometry of the Paleogene and Neogene marine units that directly lie beneath the Quaternary units. These older units include the Santee Limestone, Tupelo Bay, Parkers Ferry, Ashley, Chandler Bridge, Edisto, Parachucla, and Marks Head Formations, the Goose Creek Limestone, and the Raysor Formation. The estimated locations of deeply buried active basement faults are shown which are responsible for ongoing modern seismicity in the Charleston, S.C. area.

Database of Bacterial ExoToxins for Human is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach).

France E-Commerce Transactions: AOV: Sports: Running data was reported at 151.836 USD in 10 May 2025. This records an increase from the previous number of 146.159 USD for 09 May 2025. France E-Commerce Transactions: AOV: Sports: Running data is updated daily, averaging 129.237 USD from Dec 2018 (Median) to 10 May 2025, with 2296 observations. The data reached an all-time high of 305.213 USD in 14 Feb 2020 and a record low of 40.042 USD in 30 Jun 2020. France E-Commerce Transactions: AOV: Sports: Running data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s France – Table FR.GI.EC: E-Commerce Transactions: by Category.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

Oman E-Commerce Transactions: AOV: Sports: Running data was reported at 45.783 USD in 27 Jun 2024. This records a decrease from the previous number of 171.576 USD for 18 Nov 2023. Oman E-Commerce Transactions: AOV: Sports: Running data is updated daily, averaging 182.896 USD from Jan 2019 (Median) to 27 Jun 2024, with 111 observations. The data reached an all-time high of 803.993 USD in 02 Oct 2019 and a record low of 4.248 USD in 09 Sep 2023. Oman E-Commerce Transactions: AOV: Sports: Running data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Oman – Table OM.GI.EC: E-Commerce Transactions: by Category.

Files contain all data and code necessary to reproduce findings in the paper.

The data set comprises raw and processed lower extremity gait kinematics and kinetics signals of 39 subjects in different file formats (c3d and txt). A file of metadata (in txt and xls formats), including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes.

According to the Wikipedia, an ultramarathon, also called ultra distance or ultra running, is any footrace longer than the traditional marathon length of 42.195 kilometres (26 mi 385 yd). Various distances are raced competitively, from the shortest common ultramarathon of 31 miles (50 km) to over 200 miles (320 km). 50k and 100k are both World Athletics record distances, but some 100 miles (160 km) races are among the oldest and most prestigious events, especially in North America.}

The data in this file is a large collection of ultra-marathon race records registered between 1798 and 2022 (a period of well over two centuries) being therefore a formidable long term sample. All data was obtained from public websites.

Despite the original data being of public domain, the race records, which originally contained the athlete´s names, have been anonymized to comply with data protection laws and to preserve the athlete´s privacy. However, a column Athlete ID has been created with a numerical ID representing each unique runner (so if Antonio Fernández participated in 5 races over different years, then the corresponding race records now hold his unique Athlete ID instead of his name). This way I have preserved valuable information.

The dataset contains 7,461,226 ultra-marathon race records from 1,641,168 unique athletes.

The following columns (with data types) are included:

• Year of event (int64)
• Event dates (object)
• Event name (object)
• Event distance/length (object)
• Event number of finishers (int64)
• Athlete performance (object)
• Athlete club (object)
• Athlete country (object)
• Athlete year of birth (float64)
• Athlete gender (object)
• Athlete age category (object)
• Athlete average speed (object)
• Athlete ID (int64)

The Event name column include country location information that can be derived to a new column, and similarly seasonal information can be found in the Event dates column beyond the Year of event (these can be extracted with a bit of processing).

The Event distance/length column describes the type of race, covering the most popular UM race distances and lengths, and some other specific modalities (multi-day, etc.):

• Distances: 50km, 100km, 50mi, 100mi
• Lengths: 6h, 12h, 24h, 48h, 72h, 6d, 10d

Additionally, there is information of age, gender and speed (in km/h) in other columns.

OverviewBiomechanics dataset of human subjects (N=1798) walking and or running on a treadmill. Data include 3D marker positions over trials ranging from 25-60 seconds. Also included are demographic information and calculated variables of interest (step with, stride rate, peak knee flexion angle, etc...), sample processing code, and data analysis tutorials.This dataset accompanies an article with the following citation:Ferber R, Brett A, Fukuchi RK, Hettinga B, Osis ST. (2024). A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running. Scientific Data - Nature. 11:1232 | https://doi.org/10.1038/s41597-024-04011-7Data DescriptionContained within this dataset are 4 categories of files. They consist of datafiles (.JSON format ->2506 files), metadata (.CSV format ->2 files), Matlab processing code (.M, .MAT format -> 8 files) and Matlab tutorial files (.M, .MLX, .MAT format -> 8 files). All code which accompanies this dataset (processing and tutorials) can be found in the "supplementary_materials.zip" file.Data files are contained within the zipped folder "ric_data" which itself a contains series of folders with names representing the subject ID. Each subject ID folder contains timestamped datafile(s) in ".json" format with each containing walking and/or running data from a single collection session.MethodsThree-dimensional (3D) marker trajectory data were captured using either a 3-camera or an 8-camera VICON motion capture system (Bonita or MX3+, Vicon Motion Systems Oxford, UK) while participants walked or ran on a treadmill. Spherical retro-reflective markers were placed on anatomical landmarks and rigid plates with clusters of 3-4 markers were placed on each of seven lower body segments as per Pohl et al. (Gait Posture. 2010;32(4):559-563.). The marker-set consisted of seven rigid segments and followed International Society of Biomechanics standards. To allow for unobstructed movement during running, anatomical markers were removed following a one second static trial where subjects stood upon a template with their feet positioned straight ahead and 0.3m apart with arms crossed over their chest. Following a warmup period of 2-5mins, kinematic data were collected for approximately 60 seconds while participants walked and then ran at a self-selected speed.Data were collected at the University of Calgary Running Injury Clinic as part of research studies or as part of clinical practice between 2009 and 2017. All subjects provided informed consent and all data were collected under approval from the University of Calgary's Conjoint Health Research Ethics Board (CHREB) (Ethics IDs: E–21705, E–22194, E–24339). In total, n=1197 (67%) can be considered unique datasets and have not been published in previous scientific manuscripts. However, 33% of the dataset (n=601) were recruited for specific research studies and as such, have been used in previously published works including comparisons between recreational and competitive runners, healthy and knee osteoarthritis patients , developing novel methods for MoCap marker placement, and determination of subgroups in healthy and injured runners. Please see accompanying paper for references to these studies.More detailsMore details regarding this dataset can be found in the README file. This file contains more detailed descriptions of the contents of the datafiles, processing code, and tutorial code.LicensingThe data is protected under a CC BY 4.0 license. All scripts and functions are protected under a permissive MIT license which can be found in the file LICENSE.txt.

Get the latest USA Running Boards import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

as well as reproducing the paper

This project contains the database used to re-parametrize the ReaxFF force field for LiF, an inorganic compound. The purpose of the database is to improve the accuracy and reliability of ReaxFF calculations for LiF. The results and method used were published in the article Enhancing ReaxFF for Lithium-ion battery simulations: An interactive reparameterization protocol. This database was made using the simulation obtained using the protocol published in Enhancing ReaxFF repository. Installation To use the database and interact with it, ensure that you have the following Python requirements installed: Minimum Requirements: Python 3.9 or above Atomic Simulation Environment (ASE) library Jupyter Lab Requirements for Re-running or Performing New Simulations: SCM (Software for Chemistry & Materials) Amsterdam Modeling Suite PLAMS (Python Library for Automating Molecular Simulation) library You can install the required Python packages using pip: pip install -r requirements.txt Warning: Make sure to have the appropriate licenses and installations of SCM Amsterdam Modeling Suite and any other necessary software for running simulations. Folder Structure The project has the following folder structure: . ├── CONTRIBUTING.md ├── CREDITS.md ├── LICENSE ├── README.md ├── requirements.txt ├── assets ├── data │ ├── LiF.db │ ├── LiF.json │ └── LiF.yaml ├── notebooks │ ├── browsing_db.ipynb │ └── running_simulation.ipynb └── tools ├── db ├── plams_experimental └── scripts CONTRIBUTING.md: This file provides guidelines and instructions for contributing to the repository. It outlines the contribution process, coding conventions, and other relevant information for potential contributors. CREDITS.md: This file acknowledges and credits the individuals or organizations that have contributed to the repository. LICENSE: This file contains the license information for the repository (CC BY 4.0). It specifies the terms and conditions under which the repository's contents are distributed and used. README.md: This file. requirements.txt: This file lists the required Python packages and their versions. (see installation section) assets: This folder contains any additional assets, such as images or documentation, related to the repository. data: This folder contains the data files used in the repository. LiF.db: This file is the SQLite database file that includes the DFT data used for the ReaxFF force field. Specifically, it contains data related to the inorganic compound LiF. LiF.json: This file provides the database metadata in a human-readable format using JSON. LiF.yaml: This file also contains the database metadata in a more human-readable format, still using YAML. notebooks: This folder contains Jupyter notebooks that provide demonstrations and examples of how to use and analyze the database. browsing_db.ipynb: This notebook demonstrates how to handle, select, read, and understand the data points in the LiF.db database using the ASE database Python interface. It serves as a guide for exploring and navigating the database effectively. running_simulation.ipynb: In this notebook, you will find an example of how to get a data point from the LiF.db database and use it to perform a new simulation. The notebook showcases how to utilize either the PLAMS library or the AMSCalculator and ASE Python library to conduct simulations based on the retrieved data and then store it as a new data point in the LiF.db database. It provides step-by-step instructions and code snippets for a seamless simulation workflow. tools: This directory contains a collection of Python modules and scripts that are useful for reading, analyzing, and re-running simulations stored in the database. These tools are indispensable for ensuring that this repository adheres to the principles of Interoperability and Reusability, as outlined by the FAIR principles. db: This Python module provides functionalities for handling, reading, and storing data into the database. plasm_experimental: This Python module includes the necessary components for using the AMSCalculator with PLASM and the SCM software package, utilizing the ASE API. It facilitates running simulations, performing calculations. scripts: This directory contains additional scripts for dvanced usage scenarios of this repository. Interacting with the Database There are three ways to interact with the database: using the ASE db command line, the web interface, and the ASE Python interface. ASE db Command-line To interact with the database using the ASE db terminal command, follow these steps: Open a terminal and navigate to the directory containing the LiF.db file. Run the following command to start the ASE db terminal: ase db LiF.db You can now use the available commands in the terminal to query and manipulate the database. More information can be found in the ASE database documentation. Web Interface To interact with the database using the web interface, follow these steps: Open a terminal and navigate to the directory

This is a sample dataset that illustrates the running process of DASDC(https://github.com/soo-h/DASweepDetect). The folder of simu_config is the input from step 1 of the DASDC software,corresponds to the contents in the example/simu_config folder. The folder of simu_data is the output from step 1 of the DASDC software (that is, the input file from step 2),corresponds to the contents in the example/simu_data folder. The folder of simu_feature is the output from step 2 of the DASDC software (that is, the input file from step 3),corresponds to the contents in the example/simu_feature folder. The folder of real_feature is the output from step 3 of the DASDC software,corresponds to the contents in the example/real_feature folder. The folder of trainSet is the output from step 4 of the DASDC software (that is, the input file from step 5),corresponds to the contents in the example/trainSet folder. The folder of pred_res is the output from step 6 of the DASDC software,corresponds to the contents in the example/pred_res folder.