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

This dataset contains China Incremental Capacity Data 2008-2017. Data from Power Knowledge Thinker. Export API data for more datasets to advance energy economics research

Incremental changes in the budget from year to year, dating back to 2015-2016.

Content

Incremental changes in the budget from year to year, dating back to 2015-2016.

Context

This is a dataset hosted by the city of Los Angeles. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore Los Angeles's Data using Kaggle and all of the data sources available through the city of Los Angeles organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Peter Y. Chuang on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Individual incremental energy bid volumes and prices for automatic Frequency Restoration Reserve (aFRR) and manual Frequency Restoration Reserve (mFRR) both day-ahead and intraday - submitted by Balance responsible Parties (BRPs) and Balance Service Providers (BSPs), taking into account the known technical and contractual constraints. This report contains data for the last 2 years and is refreshed once per day.This dataset contains data from 22/05/2024 (MARI local go-live) on.

This is the SMT-LIB benchmark library: a large library of input problems, or benchmarks, written in the SMT-LIB language. More information about the SMT-LIB initiative can be found at https://smt-lib.org. The contributor and licence of each benchmark is indicated in the metadata fields of each benchmark file. This collection contains the incremental benchmarks. Benchmarks are grouped by logic and compressed with the zstd compression algorithm using the tar --zstd. SMT-LIB benchmarks usually have a very high compression rate, be aware of the required disk space when uncompressing the archives. You can find the size of each archive in compressed and uncompressed form in the following list.

Update Frequency: Update frequency: Datasets are refreshed every night to ensure the most current information is available. Even if there are no changes, the data will be updated nightly.

City of Milwaukee tax incremental district (TID) polygons. These districts are sometimes referred to as tax incremental financing districts. http://city.milwaukee.gov/TIDs.htm

This dataset contains the data analysed in the manuscript "Incremental and transformational adaptation to climate change among Indigenous Peoples and local communities: A global review". It consists of 1) a csv-file containing bibliographic information of the case studies analysed in the manuscript; 2) a csv-file containing the characteristics of the locations of the case studies analysed in the manuscript; 3) a csv-file containing the group characteristics reported in the case studies analysed in the manuscript; 4) a csv-file containing the acronyms and their explanations as used in the other csv-files.

This report contains data for the last 2 years and is refreshed once per day. Individual anonymized available incremental volumes and corresponding prices based on automatic Frequency Restoration Reserve (aFRR) and manual Frequency Restoration Reserve (mFRR)energy bids and nominations both day-ahead and intraday - submitted by Balance responsible Parties (BRPs) and Balance Service Providers (BSPs), taking into account the known technical and contractual constraints.This report is named Increment ARC Merit Order in Data Download in Elia.be.This dataset contains data until 21/05/2024 (before MARI local go-live).

Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Starter Kernels

• HistGradientBoostingClassifier Base Model

Acknowledgement

The data provided for paper: "A Large Scale Benchmark for Uplift Modeling"

https://s3.us-east-2.amazonaws.com/criteo-uplift-dataset/large-scale-benchmark.pdf

• Eustache Diemert CAIL e.diemert@criteo.com
• Artem Betlei CAIL & Université Grenoble Alpes a.betlei@criteo.com
• Christophe Renaudin CAIL c.renaudin@criteo.com
• Massih-Reza Amini Université Grenoble Alpes massih-reza.amini@imag.fr

For privacy reasons the data has been sub-sampled non-uniformly so that the original incrementality level cannot be deduced from the dataset while preserving a realistic, challenging benchmark. Feature names have been anonymized and their values randomly projected so as to keep predictive power while making it practically impossible to recover the original features or user context.

Inspiration

We can foresee related usages such as but not limited to:

• Uplift modeling
• Interactions between features and treatment
• Heterogeneity of treatment

More Readings

• Supercharging customer touchpoints with uplift modeling
• CasualML
• PyLift

MORE DATASETs ...

Update Frequency: Annual

Updated for 2022. Current and historic values of property within Tax Incremental Districts (TID) in the City of Milwaukee. Data includes historic values all TIDs, both active and inactive.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

IncRML resources

This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our experiment tool written in Python which was already used before in the Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.

Datasets

We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.

Benchmarks

GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)

GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)

GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)

Real-world datasets

Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)

Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)

Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)

Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)

Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)

Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)

OpenStreetMap (World): geographical map data (1 day, 1440 versions)

Ingestion

Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.

Remarks

The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.

GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz

All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).

Reproducing

By using our experiment tool, you can easily reproduce the experiments as followed:

Download one of the TAR.XZ archives and unpack them.

Clone the GitHub repository of our experiment tool and install the Python dependencies with 'pip install -r requirements.txt'.

Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.

Execute the tool by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run'. The argument '--runs=5' is used to perform the experiment 5 times.

Once executed, you can generate the statistics by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive stats'.

Testcases

Testcases to verify the integration of RML and LDES with IncRML, see https://doi.org/10.5281/zenodo.10171394

This report contains data for the last 2 years and is refreshed once per day. Individual anonymized available incremental volumes and corresponding prices based on automatic Frequency Restoration Reserve (aFRR) and manual Frequency Restoration Reserve (mFRR)energy bids and nominations both day-ahead and intraday - submitted by Balance responsible Parties (BRPs) and Balance Service Providers (BSPs), taking into account the known technical and contractual constraints.This report is named Increment ARC Merit Order in Data Download in Elia.be.This dataset contains data until 21/05/2024 (before MARI local go-live).

This dataset contains final results for XRF and select laboratory results for the 2019 Carson River Mercury Site Incremental Sampling Field Study. Incremental samples were collected from three separate areas within the CRMS: Six Mile Canyon Area near Mark Twain, NV; California Pan Mill in Virginia City, NV; and Sacramento Mill in Virginia City, NV. The purpose of the data collection was to help characterize the extent of Hg, Pb, and As in surface (0 to 6-inch) and in some locations subsurface (0 to 24-inch) soils in the three study areas. A secondary purpose of the study was to demonstrate incremental sampling techniques and field XRF analysis to EPA Region 9 and NDEP Staff. The XRF results columns in the attribute table were generated by XRF data collected in accordance with the EPA-Approved Quality Assurance Project Plan and are considered definitive results suitable for project decisions. 30-point incremental samples were sieved to the 100-mesh fraction, placed “interference free” XRF read bags, and analyzed with EPA Headquarters’ Niton XRF. At least two XRF measurements were collected on each side of the bag resulting in at least four readings that were used to calculate the sample bag average that appears in the XRF Results columns for Hg, Pb, and As. If triplicate results were collected for a given sample, the mean is reported in the XRF Results columns for Hg, Pb, and As and the results for each of the three replicates are detailed in the XRF Notes column.

This is the data and code for "Radical and Incremental Innovation: The Roles of Firms, Managers, and Innovators".Abstract: This paper investigates the determinants of radical (“creative”) innovations – innovations that break new ground in terms of knowledge creation. After presenting a motivating model focusing on the choice between incremental and radical innovation, and on how managers of different ages and human capital are sorted across different types of firms, we provide firm-level and patent-level evidence that firms that are more open to hiring younger managers (those that are more “open to disruption”) are significantly more likely to engage in radical innovation. Our measures of radical innovations proxy for innovation quality (average number of citations per patent) and creativity (fraction of superstar innovators, the likelihood of a very high number of citations, and generality of patents). We present robust evidence that firms that have a comparative advantage in new innovations (e.g., because they are more open to disruption) generate more creative innovations, but we also show that once the effect of the sorting of young managers to such firms is factored in, the (causal) impact of manager age on creative innovations, though positive, is small.

Thailand Change on Non Performing Loan: Incremental data was reported at 10,369.050 THB mn in Sep 2018. This records an increase from the previous number of 6,468.470 THB mn for Jun 2018. Thailand Change on Non Performing Loan: Incremental data is updated quarterly, averaging 6,287.995 THB mn from Dec 2016 (Median) to Sep 2018, with 8 observations. The data reached an all-time high of 19,912.510 THB mn in Sep 2017 and a record low of -976.750 THB mn in Mar 2017. Thailand Change on Non Performing Loan: Incremental data remains active status in CEIC and is reported by Fiscal Policy Office. The data is categorized under Global Database’s Thailand – Table TH.F028: Government Finance Institutions: Change of Non Performing Loan.

This dataset contains national designs data. Data is published in form of full export and subsequent incremental data.

This dataset accompanies the paper titled "On the Equivalence of Sensory and Incremental Nonlinear Dynamic Inversion" (paper is under review). It includes flight test data, simulation data, post-processing scripts. The Sensory Incremental Nonlinear Dynamic Inversion (sNDI) is compared to the more conventional Incremental Nonlinear Dynamic Inversion (INDI) control law. The dataset contains simulation and real outdoor testing data validating sNDI for a Vertical Take-Off and Landing platform.

3318 Global exporters importers export import shipment records of Incremental encoder with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

DB4ISF

DB4ISF is an incremental sheet forming database consisting of 76 forming experiments executed by the Chair of Production Systems at Ruhr-Universität Bochum.

The database consists of the following data:• General process data (tool radii, sheet thickness, step depth, ...)• CAD files (stl, sldprt including CAMWorks toolpaths)• Toolpaths (surface points and normal vectors)• Robot programs used for forming (KRL)• Digitization (CDB)• Deviation of every toolpath point in normal direction• Precalculated surface representations for machine learning

Publication and reference

A publication that describes the methodical approach for building up the database and the experimental data inside it can be found here:Möllensiep, Dennis; Schäfer, Jan; Pasch, Felix, Kuhlenkötter Bernd. Cluster analysis for systematic database extension to improve machine learning performance in double-sided incremental sheet forming. International Journal of Advanced Manufacturing Technology (2024). https://doi.org/10.1007/s00170-024-14014-8.

Please cite the corresponding publication too if you are using the database for your own research.

ML4ISF

ML4ISF is a Matlab Framework with a GUI for the application of machine learning in incremental sheet forming and fully compatible with the database.

The framework offers the following features:• Data management• Toolpath import with various presets• Calculation of surface representations utilized for machine learning• Generation of training data tables for machine learning in python• Prediction of the forming accuracy with several provided artificial networks• Toolpath adjustments based on the prediction and smoothing of the path• Generation of Kuka robot programs (other exporters could be implemented)• Various plotting functions

Prerequisites:• Surface toolpath with the corresponding normal vectors• STL file of the part

The framework was developed for the application of double sided incremental forming (DSIF) utilizing two industrial robots where the supporting robot applies a defined support force. Other methods such as SPIF with NC-machines can be implemented with slight modifications.

ML4ISF can be downloaded here: https://doi.org/10.5281/zenodo.10036335.

Contact

If you have any questions about the database, please contact:Dennis Möllensiepmoellensiep@lps.rub.de

License

This database is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.