Comparison of running time (seconds) of the algorithms implemented in MATLAB (upper section) and other platforms (lower section) for two real datasets respectively.

File List meta_fact.zip -- zip file containing the following eight MATLAB function files: fact_hedges_d.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using Hedges' d; the sampling variances of each effect size are also returned. fact_logRR.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using the log response ratio; the sampling variances and degrees of freedom of each effect size are also returned. J.m -- A Matlab function that computes the small-sample size correction factor J. Q.m -- A Matlab function that computes a weighted sum of squares. mean_effect.m -- A Matlab function that returns a weighted mean effect size and its 95% confidence limits, where the weights include the among-study variance if it is significant at P < 0.05. Best used when effect sizes are measured using Hedges' d; for the log response ratio, use mean_effect_L. mean_effect_L.m -- A Matlab function that returns the weighted mean log response ratio effect size, its SE, and its 95% confidence limits, where the weights include the among-study variance, the significance of which (from a chi-square test on the sum of squares) is returned as well. test_Qb_mixed_2.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing two classes. test_Qb_mixed_n.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing n classes. Description This supplement includes Matlab code to compute individual, overall, and interactive effects using Hedges’ d and the log response ratio, to calculate weighted mean effect sizes, and to perform mixed-model homogeneity tests. Functions mean_effect, mean_effect_L, test_Qb_mixed_2, and test_Qb_mixed_n all use the function chi2cdf from the Matlab Statistics Toolbox. Additional documentation appears as comments at the beginning of each function file; once the files have been downloaded into a folder in the Matlab path, typing help function_name (e.g., help fact_logRR) at the Matlab command prompt will display the descriptive comments.

PARP1 and PARP2 recognize DNA breaks immediately upon their formation, generate a burst of local PARylation to signal their location, and are co-targeted by all current FDA-approved forms of PARP inhibitors (PARPi) used in the cancer clinic. Recent evidence indicates that the same PARPi molecules impact PARP2 differently from PARP1, raising the possibility that allosteric activation may also differ. We find that unlike for PARP1, destabilization of the autoinhibitory domain of PARP2 is insufficient for DNA damage-induced catalytic activation. Rather, PARP2 activation requires further unfolding of an active site helix. In contrast, the corresponding helix in PARP1 only transiently forms, even prior to engaging DNA. Only one clinical PARPi, Olaparib, stabilizes the PARP2 active site helix, representing a structural feature with the potential to discriminate small molecule inhibitors. Collectively, our findings reveal unanticipated differences in local structure and changes in activation-coupled backbone dynamics between human PARP1 and PARP2. Methods HDExaminer software (v 2.5.0) was used, which uses peptide pool information to identify the deuterated peptides for every sample in the HXMS experiment. The quality of each peptide was further assessed by manually checking mass spectra. The level of HX of each reported deuterated peptide is corrected for loss of deuterium label (back-exchange after quench) during HXMS data collection by normalizing to the maximal deuteration level of that peptide in the fully-deuterated (FD) samples. After normalizing, we then compared the extent of deuteration to the theoretical maximal deuteration (maxD, i.e. if no back-exchange occurs). The data analysis statistics for all the protein states are in Table S2 of Smith-Pillet et al., Mol cell 2025. The difference plots for the deuteration levels between any two samples were obtained through an in-house script written in MATLAB. The script compares the deuteration levels between two samples (e.g. PARP2 and PARP2 with 5’P nicked DNA) and plots the percent difference of each peptide, by subtracting the percent deuteration of PARP2 with 5’P nicked DNA from PARP2 and plotting according to the color legend in stepwise increments. The plot of representative peptide data is shown as the mean of three independent measurements +/- SD. Statistical analysis included a t-test with a P-value <0.05. HX experiments of PARP1 with or without DNA and/or EB-47 have been published. To compare PARP1 and PARP2 datasets, HX samples of PARP1 were repeated in triplicate to have the same peptide digestions and subsequent peptide data, and HX changes in HD peptides were compared between PARP1 and PARP2 with the indicated conditions. HXMS data at 100 s for PARP2 and in the presence of gap DNA, 5’OH nicked DNA, and 5’P nicked DNA was plotted through an in-house script written in R (see Fig. S1A in Smith-Pillet et al., Mol cell 2025).

All of the datasets and the below description are quoted from Project - Data-driven prediction of battery cycle life before capacity degradation.

Objective

This dataset, used in our publication “Data-driven prediction of battery cycle life before capacity degradation”, consists of 124 commercial lithium-ion batteries cycled to failure under fast-charging conditions. These lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were cycled in horizontal cylindrical fixtures on a 48-channel Arbin LBT potentiostat in a forced convection temperature chamber set to 30°C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.

The objective of this work is to optimize fast charging for lithium-ion batteries. As such, all cells in this dataset are charged with a one-step or two-step fast-charging policy. This policy has the format “C1(Q1)-C2”, in which C1 and C2 are the first and second constant-current steps, respectively, and Q1 is the state-of-charge (SOC, %) at which the currents switch. The second current step ends at 80% SOC, after which the cells charge at 1C CC-CV. The upper and lower cutoff potentials are 3.6 V and 2.0 V, respectively, which are consistent with the manufacturer’s specifications. These cutoff potentials are fixed for all current steps, including fast charging; after some cycling, the cells may hit the upper cutoff potential during fast charging, leading to significant constant-voltage charging. All cells discharge at 4C.

The dataset is divided into three “batches”, representing approximately 48 cells each. Each batch is defined by a “batch date”, or the date the tests were started. Each batch has a few irregularities, as detailed on the page for each batch.

The data is provided in two formats. For each batch, a MATLAB struct is available. The struct provides a convenient data container in which the data for each cycle is easily accessible. This struct can be loaded in either MATLAB or python (via the h5py package). Pandas dataframes can be generated via the provided code. Additionally, the raw data for each cell is available as a CSV file. Note that the CSV files occasionally exhibit errors in both test time and step time in which the test time resets to zero mid-cycle; these errors are corrected for in the structs.

The temperature measurements are performed by attaching a Type T thermocouple with thermal epoxy (OMEGATHERM 201) and Kapton tape to the exposed cell can after stripping a small section of the plastic insulation. Note that the temperature measurements are not perfectly reliable; the thermal contact between the thermocouple and the cell can may vary substantially, and the thermocouple sometimes loses contact during cycling.

Internal resistance measurements were obtained during charging at 80% SOC by averaging 10 pulses of ±3.6C with a pulse width of 30 ms (2017-05-12 and 2017-06-30) or 33 ms (2018-04-12).

The following repository contains some starter code to load the datasets in either MATLAB or python:

https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Low rate data used to generate figure 4:

• 2018-02-20_batchdata_updated_struct_errorcorrect.mat
• 2018-04-03_varcharge_batchdata_updated_struct_errorcorrect.mat

If using this dataset in a publication, please cite: Severson et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy volume 4, pages 383–391 (2019).

**Batch - 2017-05-12**
Experimental design
- All cells were cycled with one-step or two-step charging policies. The charging time varies from ~8 to 13.3 minutes (0-80% SOC). There are generally two cells tested per policy, with the exception of 3.6C(80%).
- 1 minute and 1 second rests were placed after reaching 80% SOC during charging and after discharging, respectively.
-We cycle to 80% of nominal capacity (0.88 Ah).
- An initial C/10 cycle was performed in the beginning of each test.
- The cutoff currents for the constant-voltage steps were C/50 for both charge and discharge.
- The pulse width of the IR test is 30 ms.

Experimental notes
- The computer automatically restarted twice. As such, there are some time gaps in the data.
- The temperature control is somewhat inconsistent, leading to variability in the baseline chamber temperature.
- The tests in channels 4 and 8 did not successfully start and thus do not have data.
- The thermocouples for channels 15 and 16 were accidentally switched.

Data notes
- Cycle 1 data is not available in the struct. The sampling rate for this cycle was initially too high, so we excluded it from the data set to create more manageable file sizes.
- The cells in Channels 1, 2, 3, 5, and 6 (3.6C(80%) and 4C(80%) policies) were stopped at the end of this batch and resume...

This data set has been collected from a custom built battery prognostics testbed at the NASA Ames Prognostics Center of Excellence (PCoE). Li-ion batteries were run through 3 different operational profiles (charge, discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects. Repeated charge and discharge cycles result in accelerated aging of the batteries. The experiments were stopped when the batteries reached the end-of-life (EOL) criteria of 30% fade in rated capacity (from 2 Ah to 1.4 Ah). Data Acquisition: The testbed comprises: * Commercially available Li-ion 18650 sized rechargeable batteries, * Programmable 4-channel DC electronic load, * Programmable 4-channel DC power supply, * Voltmeter, ammeter and thermocouple sensor suite, * Custom EIS equipment, * Environmental chamber to impose various operational conditions, * PXI chassis based DAQ and experiment control, and MATLAB based experiment control, data acquisition and prognostics algorithm evaluation setup (appx. data acquisition rate is 10Hz). Parameter Description: Data Structure: cycle: top level structure array containing the charge, discharge and impedance operations type: operation type, can be charge, discharge or impedance ambient_temperature: ambient temperature (degree C) time: the date and time of the start of the cycle, in MATLAB date vector format data: data structure containing the measurements for charge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at charger (Amps) Voltage_charge: Voltage measured at charger (Volts) Time: Time vector for the cycle (secs) for discharge the fields are: Voltage_measured: Battery terminal voltage (Volts) Current_measured: Battery output current (Amps) Temperature_measured: Battery temperature (degree C) Current_charge: Current measured at load (Amps) Voltage_charge: Voltage measured at load (Volts) Time: Time vector for the cycle (secs) Capacity: Battery capacity (Ahr) for discharge till 2.7V for impedance the fields are: Sense_current: Current in sense branch (Amps) Battery_current: Current in battery branch (Amps) Current_ratio: Ratio of the above currents Battery_impedance: Battery impedance (Ohms) computed from raw data Rectified_impedance: Calibrated and smoothed battery impedance (Ohms) Re: Estimated electrolyte resistance (Ohms) Rct: Estimated charge transfer resistance (Ohms) Intended Use: The data sets can serve for a variety of purposes. Because these are essentially a large number of Run-to-Failure time series, the data can be set for development of prognostic algorithms. In particular, due to the differences in depth-of-discharge (DOD), the duration of rest periods and intrinsic variability, no two cells have the same state-of-life (SOL) at the same cycle index. The aim is to be able to manage this uncertainty, which is representative of actual usage, and make reliable predictions of Remaining Useful Life (RUL) in both the End-of-Discharge (EOD) and End-of-Life (EOL) contexts.

This replication package holds analysis data of the paper "EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects" by Sohil Lal Shrestha, Alexander Boll, Shafiul Azam Chowdhury, Timo Kehrer and Christoph Csallner.

The package contains 3 SQLite files: 1. EvoSL_36_2019a.sqlite contains analysis data of EvoSL_36 extracted using MATLAB/Simulink R2019a. Following are highlighted tables: 1.1 Model_Element_Changes : Contains unfiltered element changes of EvoSL_36 using MATLAB/Simulink R2019a 1.2 Cleaned_Model_Element_Changes : Filtered Element Changes removing duplicate changes

Other tables are directly copied from EvoSL.

1. evoSL_2019avs2022b_5sampleProjects.sqlite: contains analysis data primarily used to gauge the difference of element change data when extracted using two versions of MATLAB/Simulink (i.e., R2019a and R2022b) 2.1 model_element_changes_22b: contains element changes from EvoSL extracted using R2022b 2.2 five_sample_element_changes_19a: element changes from 5 randomly sampled EvoSL using R2019a

Comparision between the table highlights element changes using Simulink built-in comparision tool can vary widely. Use the compare2019and2022b.py on SimEvolutionTool to see the difference in results (The comparison is light weight and imprecise. More depth comparision can be done to pin point how do they differ by using the two tables.)

3 BlockTypeCategory.txt contains the full list of block types categorized into specific block category.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

This is a product of my master's work "COMPARISON BETWEEN HESITANT FUZZY LINGUISTIC VIKOR METHODS IN THE CONTEXT OF SUPPLIER SELECTION, 2022" at the Postgraduate Program in Administration at Federal Technological University of Paraná. In my study, I performed the modeling and simulation on MATLAB based on the article WU, Z.; XU, J.; JIANG, X.; ZHONG, L. Two MAGDM models based on hesitant Fuzzy linguistic term sets with possibility distributions: VIKOR and TOPSIS. Information Sciences, v. 473, p. 101-120, 2019.

We are pleased to share the dataset SEED-PQD-v1 (SEED Power Quality Distrubance Dataset v1) used in our study titled "XPQRS: Expert power quality recognition system for sensitive load applications," published in Elsevier Journal Measurement. This dataset is invaluable for researchers and practitioners in the field of power quality analysis, especially those focusing on sensitive load applications. This dataset can be used in Python as well as in MATLAB.

Access the published paper:

https://www.sciencedirect.com/science/article/abs/pii/S0263224123004530

Dataset Details:

Fundamental Frequency: 50 Hz

Sampling Rate: 5 kHz

Number of Classes: 17

Signals per Class: 1000

Length of Each Signal (samples): 100

Length of Each Signal (time): 20 ms

Amplitude of Each Signal: Scaled between -1 to 1

Data Format:

The dataset is available in two formats: MATLAB and CSV.

MATLAB File:

Filename: 5Kfs_1Cycle_50f_1000Sam_1A.mat

Structure: A matrix of dimensions (1000 x 100 x 17), where:

1000 = Signals per class

100 = Samples per signal

17 = Number of classes

Class Order:

Pure_Sinusoidal

Sag

Swell

Interruption

Transient

Oscillatory_Transient

Harmonics

Harmonics_with_Sag

Harmonics_with_Swell

Flicker

Flicker_with_Sag

Flicker_with_Swell

Sag_with_Oscillatory_Transient

Swell_with_Oscillatory_Transient

Sag_with_Harmonics

Swell_with_Harmonics

Notch

CSV Files:

Files: 17 CSV files, one for each class.

Structure: Each CSV file has dimensions (1000 x 100), where:

1000 = Signals per class

100 = Samples per signal

Usage:

This dataset is designed to support the development and testing of power quality recognition systems. The 17 classes cover a broad range of power quality disturbances, providing a comprehensive resource for training machine learning models and validating their performance in recognizing various types of power quality issues.

Acknowledgements:

All users of the dataset are advised to cite the following article:

Citation: Muhammad Umar Khan, Sumair Aziz, Adil Usman, XPQRS: Expert power quality recognition system for sensitive load applications, Measurement, Volume 216, 2023, 112889, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2023.112889. Link to the article

Thank you for your interest in our work. We hope this dataset facilitates further advancements in power quality analysis and related fields.keywords: Power Quality Recognition, Power Quality Classification, Electrical Signal Analysis, Power System Disturbances, Signal Processing, Power Quality Monitoring

Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data.

https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing

This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.

In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.

• The last digit of each acceleration file: 1, 2, 3, 4, 5
• Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial

LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.

• The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8
• Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.
• x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.

The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).

The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.

Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: (1) within a region, the train always followed the same track and (2) there are no tunnels in them so the GPS data is relatively consistent.

To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.

A data descriptor paper with details of the data collection process was published.

Please cite as

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. https://doi.org/10.1038/s41597-019-0148-9(2019)

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, https://doi.org/10.5281/zenodo.1432702(2018).

For questions or suggestions please e-mail Jingxiao Liu

This dataset was primarily designed for the Helsinki Tomography Challenge 2022 (HTC2022), but it can be used for generic algorithm research and development in 2D CT reconstruction.

The dataset contains 2D tomographic measurements, i.e., sinograms and the affiliated metadata containing measurement geometry and other specifications. The sinograms have already been pre-processed with background and flat-field corrections, and compensated for a slightly misaligned center of rotation in the cone-beam computed tomography scanner. The log-transforms from intensity measurements to attenuation data have also been already computed. The data has been stored as MATLAB structs and saved in .mat file format.

The purpose of HTC2022 was to develop algorithms for limited angle tomography. The challenge data consists of tomographic measurements of two sets of plastic phantoms with a diameter of 7 cm and with holes of differing shapes cut into them. The first set is the teaching data, containing five training phantoms. The second set consists of 21 test phantoms used in the challenge to test algorithm performance. The test phantom data was released after the competition period ended.

The training phantoms were designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contain holes. These are labeled ta, tb, tc, and td. A fifth training phantom is a solid disc with no holes. We encourage subsampling these datasets to create limited data sinograms and comparing the reconstruction results to the ground truth obtainable from the full-data sinograms. Note that the phantoms are not all identically centered.

The teaching data includes the following files for each phantom:

• The sinogram and all associated metadata (.MAT).
• A pre-computed FBP reconstruction of the phantom (.MAT and .PNG).
• A segmentation of the FBP reconstruction created with the procedure described below (.MAT and .PNG).

Also included in the teaching dataset is a MATLAB example script for how to work with the CT data.

The challenge test data is arranged into seven different difficulty levels, labeled 1-7, with each level containing three different phantoms, labeled A-C. As the difficulty level increases, the number of holes increases and their shapes become increasingly complex. Furthermore, the view angle is reduced as the difficulty level increases, starting with a 90 degree field of view at level 1, and reducing by 10 degrees at each increasing level of difficulty. The view-angles in the challenge data will not all begin from 0 degrees.

The test data includes the following files for each phantom:

• The full sinogram and all associated metadata (.MAT).
• The limited angle sinogram and all associated metadata, used to test the algorithms submitted to the challenge (.MAT).
• A pre-computed FBP reconstruction of the phantom using the full data (.MAT and .PNG).
• A pre-computed FBP reconstruction of the phantom using the limited angle data. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).
• A segmentation of the FBP reconstruction using the full data, created with the procedure described below. This was used as the ground truth reference in the challenge (.MAT and .PNG).
• A segmentation of the FBP reconstruction using the limited angle data, created with the procedure described below. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).
• A photograph of the phantom, rotated and resized to match the ground truth segmentation (.PNG).

Also included in the test dataset is a collage in .PNG format, showing all the ground truth segmentation images and the photographs of the phantoms together.

As the orientation of CT reconstructions can depend on the tools used, we have included the example reconstructions for each of the phantoms to demonstrate how the reconstructions obtained from the sinograms and the specified geometry should be oriented. The reconstructions have been computed using the filtered back-projection algorithm (FBP) provided by the ASTRA Toolbox.

We have also included segmentation examples of the reconstructions to demonstrate the desired format for the final competition entries. The segmentation images for obtained by the following steps:
1) Set all negative pixel values in the reconstruction to zero.
2) Determine a threshold level using Otsu's method.
3) Globally threshold the image using the threshold level.
4) Perform a morphological closing on the image using a disc with a radius of 3 pixels.

The competitors were not obliged to follow the above procedure, and were encouraged to explore various segmentation techniques for the limited angle reconstructions.

For getting started with the data, we recommend the following MATLAB toolboxes:

HelTomo - Helsinki Tomography Toolbox
https://github.com/Diagonalizable/HelTomo/

The ASTRA Toolbox
https://www.astra-toolbox.com/

Spot – A Linear-Operator Toolbox
https://www.cs.ubc.ca/labs/scl/spot/

Using the above toolboxes for the Challenge was by no means compulsory: the metadata for each dataset contains a full specification of the measurement geometry, and the competitors were free to use any and all computational tools they want to in computing the reconstructions and segmentations.

All measurements were conducted at the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki.

Spatiotemporal measurement of surfactant distribution on gravity–capillary wavesProvides two Matlab-formated (.mat) data files containing the surface height and surfactant concentration profiles, one for each of the two datasets presented in the paper. The README.txt file contains instructions about how to read the data into Matlab and visualize it in a manner similar to the paper figures.Strickland-2015-JFM.zip

Data used to construct Fig 3b in Nature Comms 2017 doi: 10.1038/s41467-017-01962-x, PMID: 29170434.

Filenames: XYZ_early_embryo.mat,XYZ_late_embryo.mat, XYZ_S2.mat

Matlab files contain the XYZ foci position for each cell in the three analysed cell types (186, 195 and 196 cells for early embryo, late embryo and S2 cells, respectively). The XYZ positions are in nanometers.

Datasets Figure 1 in Cattoni, et al. Nature Comms 2017 doi: 10.1038/s41467-017-01962-x, PMID: 29170434.

Datasets for these figure are provided in the files: All_libraries_S2_16_11_2017.mat All_libraries_early_16_11_2017.mat
All_libraries_late_embryo_16_11_2017.mat

To retrieve the distance distributions, - load dataset in matlab - the structure 'Alllibraries' contains all data. - to retrieve pairwise distances (in nm) between libraries 2 and 3, do

Alllibraries{2,3}.distances

The different library combinations in the dataset are described in the file: All_libraries_content.csv

have fun!

This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:

measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.

Predictions provided by the models used by the Blind Test participants

Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants

Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel.

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]DAISY[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]DAISY[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

Antibiotic resistance is a leading cause of hospitalization and death worldwide. Heavy metals such as arsenic have been shown to drive co-selection of antibiotic resistance, suggesting arsenic-contaminated drinking water is a risk factor for antibiotic resistance carriage. This study aimed to determine the prevalence and abundance of antibiotic-resistant Escherichia coli (AR-Ec) among people and drinking water in high (Hajiganj, >100 μg/L) and low arsenic-contaminated (Matlab, <20 μg/L) areas in Bangladesh. Drinking water and stool from mothers and their children (<1 year) were collected from 50 households per area. AR-Ec was detected via selective culture plating and isolates were tested for antibiotic resistance, arsenic resistance, and diarrheagenic genes by PCR. Whole-genome sequencing (WGS) analysis was done for 30 E. coli isolates from 10 households. Prevalence of AR-Ec was significantly higher in water in Hajiganj (48%) compared to water in Matlab (22%, p <0.05) and among children in Hajiganj (94%) compared to children in Matlab (76%, p <0.05), but not among mothers. A significantly higher proportion of E. coli isolates from Hajiganj were multidrug-resistant (83%) compared to isolates from Matlab (71%, p <0.05). Co-resistance to arsenic and multiple antibiotics (MAR index >0.2) was observed in a higher proportion of water (78%) and child stool (100%) isolates in Hajiganj than in water (57%) and children (89%) in Matlab (p <0.05). The odds of arsenic-resistant bacteria being resistant to third-generation cephalosporin antibiotics were higher compared to arsenic-sensitive bacteria (odds ratios, OR 1.2–7.0, p <0.01). WGS-based phylogenetic analysis of E. coli isolates did not reveal any clustering based on arsenic exposure and no significant difference in resistome was found among the isolates between the two areas. The positive association detected between arsenic exposure and antibiotic resistance carriage among children in arsenic-affected areas in Bangladesh is an important public health concern that warrants redoubling efforts to reduce arsenic exposure.

This dataset is the processed model data for "Two Key Mechanisms of cross-shelf penetrating fronts in the East China Sea：Flow Convergence and Thermocline Undulation". The article is intended to submit to JGR: Ocean. The data is the output from a data assimilative model using ROMS 4D-Var. Details of the data assimilative model are shown in He et al. (2022). As the original data accout for too much strorage space, this dataset stores the daily averaged subtidal data. The processed data are in matlab format. Each file contains all variable in a month. The variable "DayNum" represents the time in the format of "days since January 0, 0000". The date can be obtained by the matlab "datestr" function. The grid information is saved in a matlab structure. See the "get_roms_grid" function in the ROMS matlab tools for details. Reference: He, Z., Yang, D., Wang, Y., & Yin, B. (2022). Impact of 4D-Var data assimilation on modelling of the East China Sea dynamics. Ocean Modelling, 102044. doi: https://doi.org/10.1016/j.ocemod.2022.102044

DOM UV-visible absorbance and fluorescence characteristics were determined for 971 samples collected from a variety of surface water locations throughout the Great Lakes Region and Southern Ontario & Quebec Regions of North America.Included in this repositoryThe final saved Matlab data project for the model (11Jan2010Model7Ex250Em300AllData.mat)The original EEMs read into Matlab (OriginalData.mat & matlab.mat)The EEMs after removal of specific wavelengths and samples (CutData.mat)The split-half validation of the full model (SplitHalfTest1.mat)The Output of the full model and split-half validation (Model7Ex250Em300AllData_ResultsOutput.xlsxSpiltHalfValidation_FinalModel7.xls) &Contour plot images of the full modelData used to construct the original parallel factor analysis model (PARAFAC) were treated as follows:Light absorbance was measured from 800 to 230 nm using a lambda 25 Perkin Elmer spectrophotometerFluorescence excitation emission matrix (EEM) scans were measured using a Varian Cary Eclipse fluorometerScan were made from 230 to 500 by 5 nm excitation and 270 to 600 by 2 nm emission with a bandwidth of 5 nm and at a scanning interval of 0.25 seconds.EEMs were corrected fully for inner filter effects, Milli-Q background, blank subtracted and instrument bias following the recommendations of (Cory et al. 2010; Murphy et al. 2010).Relative fluorescence units were converted to Raman units using the area under the Milli-Q scatter peak at 350 nm excitation.For Parallel factor analysis (PARAFAC) modelingThe DOMFluorv1_7 toolbox in Matlab 2007b (Mathworks) was used for analysis following the PARAFAC tutorial of Stedmon and Bro (2008).Note: DOMFluor is no longer compatible with current versions of Matlab and has been replaced by the drEEM toolboxDOM samples. Prior to modeling, EEMs were trimmed to 250–500 excitation and 300–600 emission, first-order scatter was removed, and outlier EEMs were deleted.The PARAFAC model was validated using split-half analysis and Tucker congruence.The model was originally published in Williams, C.J., P.C. Frost and M.A. Xenopoulos. 2013. Beyond best management practices: Pelagic biogeochemical dynamics in urban stormwater ponds. Ecological Applications 23: 1384-1395. and is published on OpenFluor as GreatLakesRegion to be compared with other PARAFAC modelsThis model has been used in multiple manuscripts and used to fit over 2000 EEMs collected after the initial model generationKing, S.S.E., P.C. Frost, S.B. Watson, and M.A. Xenopoulos. Transitions in dissolved organic phosphorus and dissolved organic carbon across a river-lake transect.Begum, Most S., M. Kadjeski, C. Fasching and M.A. Xenopoulos. Temporal variability of dissolved organic matter composition export in streams.Klemet-N’Guessan, S., M. Taskovic, N.J.T. Pearce and M.A. Xenopoulos. Fine ecological scales highlight the nonlinear relationship of animal nutrient excretion with dissolved organic matter.Pearce, N.J.T., J.H. Larson, M.A. Evans, S.W. Bailey, P.C. Frost, W.F. James, and M.A. Xenopoulos. 2023. Dissolved organic matter transformations in a freshwater rivermouth. Biogeochemistry https://doi.org/10.1007/s10533-022-01000-zWilliams, C.J., P.C. Frost, B. Ginn, D. Lembcke, J. Marsalek, and M.A. Xenopoulos. 2023. Add a dash of salt? Effects of road de-icing salt (NaCl) on benthic respiration and nutrient fluxes in freshwater sediments. Limnetica. DOI: 10.23818/limn.42.17Pearce, N.J.T., Dyczko, J.M. and M.A. Xenopoulos. 2022. Carbon and nutrients regulate greenhouse gas fluxes from oxic stream sediments. Biogeochemistry 160: 275-287 https://doi.org/10.1007/s10533-022-00955-3Pearce, N.J.T., J.H. Larson, M.A. Evans, P.C. Frost, and M.A. Xenopoulos. 2021. Episodic nutrient addition affects water column nutrient processing rates in river-to-lake transitional zones Journal of Geophysical Research: Biogeosciences 126: e2021JG006374; DOI: 10.1029/2021JG006374Kadjeski, M., C. Fasching and M.A. Xenopoulos. 2020. Synchronous biodegradability and production of dissolved organic matter in two streams of varying land use. Frontiers in Microbiology 11: 568629 (doi: 10.3389/fmicb.2020.568629).Larson, J.H., W.F. James, F.A. Fitzpatrick, P.C. Frost, M.A. Evans, P.C. Reneau, and M.A. Xenopoulos. 2020. Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA). Biogeochemistry 147: 179-197.Fasching, C., C. Akotoye, M. Bižić-Ionescu, J. Fonvielle, D. Ionescu, S. Mathavarajah, L. Zoccarato, D.A. Walsh, H.-P. Grossart and M.A. Xenopoulos. 2020. Linking stream microbial community functional genes to dissolved organic matter and inorganic nutrients. Limnology and Oceanography. 65: S71-S87.D’Amario, S.C., D.C. Rearick, C. Fasching, S. Kembel, E. Porter-Goff, D. Spooner, C.J. Williams, H.F. Wilson and M.A. Xenopoulos. 2019. The prevalence of non-linearity and detection of ecological thresholds across a land use gradient. Scientific Reports 9: 3878 (DOI: 10.1038/s41598-019-40349-4).Williams, C.J., P.C. Frost, A.M. Morales-Williams, J.H. Larson, W.B. Richardson, A.S. Chiandet and M.A. Xenopoulos. 2016. Human activities cause distinct dissolved organic matter composition across freshwater ecosystems. Global Change Biology 22: 613-626.Larson, J.H., P.C. Frost, M.A. Xenopoulos, C.J. Williams, A.M. Morales-Williams, J. Vallazza, J.C. Nelson and W.B. Richardson. 2014. Relationships between land cover and dissolved organic matter change along the river to lake transition. Ecosystems 17: 1413-1425Williams, C.J., J.E. Van Eindhoven, E. Theberge, and M.A. Xenopoulos. 2014. Effects of golf course facilities on stream function in anthropogenically impacted streams. Anthropocene 3: 51-60.

Code and data used in the production of this manuscript: MATLAB results for all methods except SINDy.