This file describes where to find the dataset used for this paper (PurpleAir and AQS) and the data fields used in the analysis. Contact the corresponding author for access to the code used to generate the dataset. This dataset is associated with the following publication: deSouza, P., K. Barkjohn, A. Clements, J. Lee, R. Kahn, and B. Crawford. An analysis of degradation in low-cost particulate matter sensors. Environmental Science: Atmospheres. Royal Society of Chemistry, Cambridge, UK, NA, (2023).

The global targeted protein degradation (TPD) market is projected to reach a value of XXX million by 2033, growing at a CAGR of XX% during the forecast period. TPD is an innovative therapeutic approach that selectively degrades specific disease-causing proteins to treat various diseases. The growing prevalence of chronic diseases, unmet medical needs, and advancements in biotechnology are driving the growth of the TPD market. The emerging trend of precision medicine and the increasing research and development efforts by pharmaceutical companies are further contributing to the market expansion. The TPD market is segmented based on application, type, and region. The major applications of TPD include oncology, neurodegenerative diseases, metabolic disorders, and infectious diseases. The market is dominated by the oncology segment, owing to the high prevalence of cancer and the significant potential of TPD in treating various cancer types. The different types of TPD technologies include proteolysis-targeting chimeras (PROTACs), molecular glues, targeted protein degradation (TPD) antibodies, and autophagy modulators. The market is geographically segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America currently holds the largest market share due to the presence of key players, high healthcare spending, and advanced research infrastructure. However, the Asia Pacific region is expected to experience the highest growth rate during the forecast period due to the increasing disease burden, rising healthcare awareness, and government initiatives to improve healthcare access in developing countries.

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the: Development of robot health monitoring algorithms and tools Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM) Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation. For data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx). If you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov.

NTA output of 6:2 FTS microbial cultures Chu TAMU. This dataset is associated with the following publication: Yang, S., Y. Shi, M. Strynar, and K. Chu. Desulfonation and defluorination of 6:2 fluorotelomer sulfonic acid (6:2 FTSA) by Rhodococcus jostii RHA1: Carbon and sulfur sources, enzymes, and pathways. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 423(Part A): 127052, (2022).

These data has been collected in study area for testing some retoration techniques

This shapefile identifies areas within the 6 LESTARI landscapes where land degradation or deforestation occurred over several time-periods (1990-1996; 1996-2000; 2000-2003; 2003-2006; 2006-2009; 2009-2011; 2011-2012).

Metadata and annotation of the reads obtained from the rice straw degradation process using Kraken2/Bracken.

This paper proposes the experiments and setups for studying diagnosis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and power MOSFET’s have higher failure rates than other components in DC-DC converter systems. Currently, our work focuses on experimental analysis and modeling electrolytic capacitors degradation and its effects on the output of DC-DC converter systems. The output degradation is typically measured by the increase in Equivalent series resistance and decrease in capacitance leading to output ripple currents.Typically, the ripple current effects dominate, and they can have adverse effects on downstream components. A model based approach to studying degradation phenomena enables us to combine the physics based modeling of the DC-DC converter with physics of failure models of capacitor degradation, and predict using stochastic simulation methods how system performance deteriorates with time. Degradation experiments were conducted where electrolytic capacitors were subjected to electrical and thermal stress to accelerate the aging of the system. This more systematic analysis may provide a more general and accurate method for computing the remaining useful life (RUL) of the component and the converter system.

Beta Dataset Road Degradation

## Overview

Beta Dataset Road Degradation is a dataset for object detection tasks - it contains Road Degradation annotations for 815 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Intro

Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185

Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.

Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.

Experiment

SOC Window

Cycles per ageing set

Current

Temperature

Number of Cells

1

0-30%

257

0.3C / 1D

10°C

3

25°C

3

40°C

3

2,2

70-85%

515

0.3C / 1D

10°C

2

25°C

2

40°C

2

3

85-100%

515

0.3C / 1D

10°C

3

25°C

3

40°C

3

4

0-100% (drive-cycle)

78

0.3C / noisy D

10°C

3

25°C

2

40°C

3

5

0-100%

78

0.3C / 1D

10°C

3

25°C

2

40°C

3

Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.

The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.

An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.

Data

Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.

Summary Data

This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.

It contains:

Performance Summary

A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.

Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our ACS publication for more details.

Data includes:

· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.

· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.

· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.

· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.

· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).

· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).

· Energy throughput: as with "charge throughput", but for energy.

· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.

· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.

· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).

· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.

· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.

· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.

· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.

Ageing Sets Summary

Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.

Processed Timeseries data

Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.

Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.

Raw Data

These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.

The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').

Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.

The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the Galvani package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.

File Naming Convention

The raw datafiles are named with a standard format. This is:

    NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1

    {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}

{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number or aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}

The Targeted Protein Degradation Market report segments the industry into By Type (Degronimids, Immunomodulatory Drugs (IMiDs), Proteolysis-Targeting Chimeric Molecules (PROTACs), and more), By Applications (Drug Discovery, Therapeutics), By End-User (Pharmaceutical and Biotechnology Companies, Academic and Research Institutes, and more), and Geography (North America, Europe, Asia-Pacific, and more).

Table S1 The u values and the z values for all gypsy moth samples Table S2 The u values of some samples in this study Table S3 The z values of some samples in this study Table S4 The ratio of u values and the ratio of z values for the folded and spread specimens Table S5 The ratio of the u values and the ratio of z values for formalin-fixed larvae and air-dried adult specimens

Table S6 All primers used in this study

We provide two spatial distributions on the degradation state within Central Asia between 2000 and 2019. The file Degradation_status represents the overall degradation extent within the region (The file degradation_state_legend.txt provides the legend) . The file Degradation_status_soils depicts land degradation status considering soil quility. Soil quality is represented by regionally specific integrated indicator on soil quality (so-called "soil-bonitet"). The file Degradation_status_soils_legend provides the legend for the raster. All files are in the geotiff format and can be opened in standard GIS software.

Tiff files: Maps of Above Ground Biomass change (2019-2020) over the study region near Iñapari, Peru, derived from the texture of the NIR band for SPOT-7 (SPOT_DeltaAGB_Map), PlanetScope (PlanetScope_DeltaAGB_Map.tif) and Sentinel-2 (Sentinel2_DeltaAGB_Map.tif) data for a 1-ha resolution.QML file contains the style for the biomass change maps. Shapefile contains location of four selectively logged plots.CSV file contains data on observed changes in these four plots, obtained by TLS and manual inventory.

The dataset consists of data used for analysing a number of models, each related to a specific failure mode or failure mechanism that affects the material properties of building materials. Three RIBuild partners (KUL, UNIVPM, RISE) were responsible of performing laboratory tests to evaluate the models chosen to characterize a specific failure mode (frost, algae, mould). One RIBuild partner (DTU/AAU) used measurement data from a WP3 test setup to validate simulations of wood rot in wooden beam ends.

Further details to be found in RIBuild deliverable D2.2.

Overview of data files to be found in 'RIBuild data WP2 Model analysis' as part of this dataset.

The Targeted Protein Degradation Market Share size and share are expected to exceed USD 3547.91 million by 2034, with a compound annual growth rate (CAGR) of 20.7% .

This layer contains information about the land degradation phenomenon observed during the Integrated Context Analysis (ICA) run in Lesotho in 2015. Data source: NASA MODIS 2001-2012. The key indicator used for the analysis was the average ecological changes observed between 2001 and 2012 through remotely sensed data. It should be noted that the land degradation analysis consisted of an erosion propensity estimation, but given the low percentages of province surface erosion-prone, the map was not included in the final report.

The global Small Molecule Protein Degradation Agent market size was valued at USD 18.44 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 17.5% from 2023 to 2030. The growth of the market is primarily driven by the rising prevalence of cancer and the increasing demand for targeted therapies. Small molecule protein degradation agents are emerging as a promising therapeutic approach for treating cancer and other diseases by selectively targeting and degrading disease-causing proteins. Key market players are focusing on developing novel small molecule protein degradation agents with improved potency, selectivity, and safety profiles. Additionally, the increasing collaborations and partnerships between pharmaceutical companies and research institutions are expected to accelerate the development and commercialization of these agents. The market is expected to witness a significant influx of new products, driven by the unmet medical needs and the promising clinical potential of these agents.

The data provided here contains the information of the publication "High temperature in situ gas analysis for identifying degradation mechanisms of lithium-ion batteries" in Chemical Science. Two excel files are attached, which provide the data for the figures in the main text amd the supporting information. Data processing is described in the paper as well as the supporting information.

This paper proposes a combined energy-based model with an empirical physics of failure model for degradation analysis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and MOSFET’s have higher failure rates than other components in DC-DC converter systems. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system causing the aircraft to fly off course. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of electrical _domain systems. Our current work adopts a physics of failure model (Arrhenius Law) for equivalent series resistance (ESR) increase in electrolytic capacitors subjected to electrical and thermal stresses. Experiments for capacitor degradation were conducted for collecting degradation ESR data. Parameter re-estimation for the failure model is done using the experimental data. The derived degradation model of the capacitor is reintroduced into the DC-DC converter system model to study changes in the system performance using Monte Carlo simulation methods. Stochastic simulation methods applied to the combined model help us predict how system performance deteriorates with time.