Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

‘○’ reflects the implementation of truly unobserved data. ‘×’ indicates the implementation of error-prone data without measurement error correction. ‘−’ represents no value. A pair (x, y) in the column (π11, π00) is the implementation of the corrected control chart with parameter values (π11, π00) = (x, y) in (10) accommodated.

{# General information# The script runs with R (Version 3.1.1; 2014-07-10) and packages plyr (Version 1.8.1), XLConnect (Version 0.2-9), utilsMPIO (Version 0.0.25), sp (Version 1.0-15), rgdal (Version 0.8-16), tools (Version 3.1.1) and lattice (Version 0.20-29)# --------------------------------------------------------------------------------------------------------# Questions can be directed to: Martin Bulla (bulla.mar@gmail.com)# -------------------------------------------------------------------------------------------------------- # Data collection and how the individual variables were derived is described in: #Steiger, S.S., et al., When the sun never sets: diverse activity rhythms under continuous daylight in free-living arctic-breeding birds. Proceedings of the Royal Society B: Biological Sciences, 2013. 280(1764): p. 20131016-20131016. # Dale, J., et al., The effects of life history and sexual selection on male and female plumage colouration. Nature, 2015. # Data are available as Rdata file # Missing values are NA. # --------------------------------------------------------------------------------------------------------# For better readability the subsections of the script can be collapsed # --------------------------------------------------------------------------------------------------------}{# Description of the method # 1 - data are visualized in an interactive actogram with time of day on x-axis and one panel for each day of data # 2 - red rectangle indicates the active field, clicking with the mouse in that field on the depicted light signal generates a data point that is automatically (via custom made function) saved in the csv file. For this data extraction I recommend, to click always on the bottom line of the red rectangle, as there is always data available due to a dummy variable ("lin") that creates continuous data at the bottom of the active panel. The data are captured only if greenish vertical bar appears and if new line of data appears in R console). # 3 - to extract incubation bouts, first click in the new plot has to be start of incubation, then next click depict end of incubation and the click on the same stop start of the incubation for the other sex. If the end and start of incubation are at different times, the data will be still extracted, but the sex, logger and bird_ID will be wrong. These need to be changed manually in the csv file. Similarly, the first bout for a given plot will be always assigned to male (if no data are present in the csv file) or based on previous data. Hence, whenever a data from a new plot are extracted, at a first mouse click it is worth checking whether the sex, logger and bird_ID information is correct and if not adjust it manually. # 4 - if all information from one day (panel) is extracted, right-click on the plot and choose "stop". This will activate the following day (panel) for extraction. # 5 - If you wish to end extraction before going through all the rectangles, just press "escape". }{# Annotations of data-files from turnstone_2009_Barrow_nest-t401_transmitter.RData dfr-- contains raw data on signal strength from radio tag attached to the rump of female and male, and information about when the birds where captured and incubation stage of the nest1. who: identifies whether the recording refers to female, male, capture or start of hatching2. datetime_: date and time of each recording3. logger: unique identity of the radio tag 4. signal_: signal strength of the radio tag5. sex: sex of the bird (f = female, m = male)6. nest: unique identity of the nest7. day: datetime_ variable truncated to year-month-day format8. time: time of day in hours9. datetime_utc: date and time of each recording, but in UTC time10. cols: colors assigned to "who"--------------------------------------------------------------------------------------------------------m-- contains metadata for a given nest1. sp: identifies species (RUTU = Ruddy turnstone)2. nest: unique identity of the nest3. year_: year of observation4. IDfemale: unique identity of the female5. IDmale: unique identity of the male6. lat: latitude coordinate of the nest7. lon: longitude coordinate of the nest8. hatch_start: date and time when the hatching of the eggs started 9. scinam: scientific name of the species10. breeding_site: unique identity of the breeding site (barr = Barrow, Alaska)11. logger: type of device used to record incubation (IT - radio tag)12. sampling: mean incubation sampling interval in seconds--------------------------------------------------------------------------------------------------------s-- contains metadata for the incubating parents1. year_: year of capture2. species: identifies species (RUTU = Ruddy turnstone)3. author: identifies the author who measured the bird4. nest: unique identity of the nest5. caught_date_time: date and time when the bird was captured6. recapture: was the bird capture before? (0 - no, 1 - yes)7. sex: sex of the bird (f = female, m = male)8. bird_ID: unique identity of the bird9. logger: unique identity of the radio tag --------------------------------------------------------------------------------------------------------}

United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms was 328.90000 Index Dec 1983=100 in September of 2018, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms reached a record high of 329.80000 in July of 2018 and a record low of 100.00000 in December of 1983. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms - last updated from the United States Federal Reserve on November of 2025.

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered conventional and continuous oil and gas resources in in the Lewis Shale of the Southwestern Wyoming Province, Wyoming and Colorado. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border a Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. In addition to the shapefile, for U.S. assessments, allocation tables are provided that enumerate percentages assigned to various land categories. Machine-readable tables are also provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

These data contain a subset of time-dependent glacier model output variables.

Reference:

Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., and Preusser, F.: Modelling last glacial cycle ice dynamics in the Alps, The Cryosphere, 12, 3265-3285, doi:10.5194/tc-12-3265-2018, 2018.

File names:

alpcyc.{1km|2km}.{epic|grip|md01}.{cp|pp}.{ex.100a|ex.1ka|ts.10a}.nc

Horizontal resolution:

1km: 1 km horizontal resolution

2km: 2 km horizontal resolution

Temperature forcing:

epic: EPICA ice core temperature forcing

grip: GRIP ice core temperature forcing

md01: MD01-2444 core temperature forcing

Precipitation forcing:

cp: constant precipitation

pp: palaeo-precipitation reduction

Variable types:

ex.100a: spatial diagnostics every hundred years

ex.1ka: spatial diagnostics every thousand years

ts.10a: scalar time-series every ten years

Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (.ex..nc) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:

gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif

The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. The band number equals 120 minus the age in ka. Band information can be displayed with:

gdalinfo NETCDF:filename.nc:variable

Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see aggregated variables.

Changelog:

Version 3:

Add spatial diagnostics every hundred years (*.ex.100a.nc)

Version 2:

Add age coordinate in kiloyears (ka) before present.

Replace NCO by Xarray workflow (no effect on the results).

Version 1:

Initial version.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Continuing Jobless Claims in the United States increased to 1960 thousand in the week ending November 15 of 2025 from 1953 thousand in the previous week. This dataset provides the latest reported value for - United States Continuing Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset consists of images and keyed data of daily precipitation strip charts for the country of El Salvador and have a period of record ranging from 1984 to 2010. The strip charts were rescued and imaged by the International Environmental Data Rescue Organization (IEDRO). These precipitation chart forms contain continuous ink traces representing the instantaneous measurement of rainfall amounts for a 24-hour period. The chart form background has a calibrated grid (usually in mm or inches) superimposed on the chart.

Traffic counts data for NJ DOT. The data sets includes short term counts (48 hours volumes) and continuous data.

These data contain a subset of time-dependent glacier model output variables. The ghf70 data files are an update on the reference below, fixing significant problems affecting the computation of the bedrock deformation in response to ice load (PISM Github issues #370 and #377) and the computation of ice temperature (PISM Github issue #371). The other data files additionally include spatially-variable geothermal heat flux (dav13, gou11comb, gou11simi, sha04), different lithospheric rigidity (eet30km) or mantle viscosity (num1e21), and higher horizontal resolution (3km).

Reference:

Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. and Kleman, J.: Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, The Cryosphere, 10(2), 639–664, doi:10.5194/tc-10-639-2016, 2016.

File names:

cisbed.{res}.{forcing}.{ex.100a|ts.10a}.{ghf}.{props}.nc

Horizontal resolution:

10km: 10 km horizontal resolution

5km: 5 km horizontal resolution

3km: 3 km horizontal resolution

Temperature forcing:

epica: EPICA ice core temperature forcing

grip: GRIP ice core temperature forcing

Variable types:

ex.100a: spatial diagnostics every hundred years

ts.10a: scalar time-series every ten years

Geothermal heat flow:

ghf70: constant 70 mW m-2 heat flow

dav13: Davies (2013) geothermal heat flow map

gou11comb: Goutorbe et al. (2011) best combination method

gou11simi: Goutorbe et al. (2011) similarity method

sha04: Shapiro and Ritzwoller (2004) heat flow map

Bedrock properties

eet30km: lithosphere elastic thickness of 30 km

num1e21: astenosphere viscosity of 1e21 Pa s

Data format:

The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (*.ex.100a.nc) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:

gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif

The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. Band information can be displayed with:

gdalinfo NETCDF:filename.nc:variable

Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata.

Funding:

Swiss National Supercomputing Centre (CSCS) grants s573 and sm13 to J. Seguinot, Swiss National Science Foundation (SNSF) grants no.~200020-169558 and 200021-153179/1 to M. Funk, and Research Foundation – Flanders (FWO) Odysseus Type II project G0DCA23N 'GlaciersMD' to H. Zekollari.

Changelog:

Version 1:

Initial version.

I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1h Data: Raw 1h historical data from multiple exchanges, covering the entire trading history of ADAUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken ADAUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/A0xoVAt.png" alt="ADAUSD Dataset Summary">

https://i.imgur.com/nIpMGw4.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)

Employment: Industry data was reported at 11,498.000 Person th in Aug 2021. This records an increase from the previous number of 11,238.000 Person th for Jul 2021. Employment: Industry data is updated monthly, averaging 11,931.000 Person th from Mar 2012 (Median) to Aug 2021, with 114 observations. The data reached an all-time high of 13,373.000 Person th in Oct 2014 and a record low of 10,507.000 Person th in Aug 2020. Employment: Industry data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

Snapshot img

Chart Recorder Market Size 2024-2028

The chart recorder market size is forecast to increase by USD 464.5 million at a CAGR of 5.9% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for multi-channel recording solutions, particularly in sectors like water purification systems where accurate data recording is essential for monitoring system performance. Strip chart recorders and circular chart recorders continue to be popular choices for continuous analog record keeping. However, the emergence of data loggers and automated data acquisition systems has introduced digital file storage as a viable alternative, offering enhanced efficiency and accuracy. Multi-pen recorders offer the advantage of recording multiple data streams on a single chart, which is beneficial in applications such as water quality monitoring. The market is also witnessing the introduction of web-based chart recorders, enabling remote monitoring and real-time data access. Despite these advancements, challenges such as the availability of substitutes and the need for calibration and maintenance persist. Overall, the market is expected to grow as industries, including those involved in water purification, continue to prioritize accurate and efficient data recording solutions.

What will the size of the market be during the forecast period?

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The market for chart recorders, a vital component of data acquisition systems (DAQ), continues to gain traction in various industries, including manufacturing, science, engineering labs, and power plants. These instruments are essential for capturing, recording, and analyzing electrical signals and process parameters such as temperature, pressure, flow, pH, humidity, vibration, movement, diagnostics, and statistical analysis. Chart recorders offer high resolution visualization tools that enable real-time monitoring and analysis of data. Their applications span across numerous sectors, including water purification, where they help monitor turbidity, dissolved oxygen, and sterilization processes. In environmental testing, they assist in tracking the effectiveness of various processes and maintaining optimal conditions. In the manufacturing sector, chart recorders play a crucial role in equipment maintenance and power plant operations.

Moreover, in the context of single-channel and multi-channel applications, chart recorders cater to diverse requirements, offering flexibility and scalability. The demand for chart recorders is driven by the increasing need for accurate and reliable data acquisition and analysis in various industries. As the importance of data-driven decision-making continues to grow, the market for these instruments is expected to expand. Furthermore, the integration of advanced features, such as statistical analysis and diagnostics, enhances their value proposition, making them indispensable tools for various applications.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Digital chart recorders
  Analog chart recorders


Application

  Food and beverage
  Pharmaceuticals
  Industrial applications
  Environmental monitoring


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France
    Italy


  South America



  Middle East and Africa

By Type Insights

The digital chart recorders segment is estimated to witness significant growth during the forecast period. Digital chart recorders are an essential component of the expanding market, providing sophisticated features and enhanced functionality for modern data recording applications. One notable example is the OMEGA RD8250 by Omega Engineering Inc., which caters to diverse industrial requirements. This advanced digital process recorder boasts dual-function keys and a clear, colored graph display, ensuring a user-friendly experience. The RD8250 offers the flexibility to display real-time data in both digital and trend formats, making it a versatile tool for monitoring temperatures and other vital parameters in dispersed systems, marine installations, and large engines. The front-panel USB port enables seamless data transfer to a PC, enabling efficient analysis and management.

Real-time data can be viewed in both digital and trend formats, ensuring versatility and accuracy in monitoring. The front-panel USB port enables seamless data transfer to a PC via a flash memory card, streamlining data management and analysis. Multi-channel strip chart recorders, circular chart recorders, data loggers, and automated data acquisition systems are other essential components of the digital chart recorder market.

This dataset is a continuous parameter grid (CPG) of mean depth to water table in the Pacific Northwest. Source data come from the Digital General Soil Map of the United States, produced by the Natural Resources Conservation Service, United States Department of Agriculture.

A table summarizing odds ratios of fecundity from a parallel MasterBayes analysis with dispersal distance fitted as a continuous variable.

The global strip chart recorder market is experiencing steady growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, considering a conservative estimate based on typical CAGR for industrial instrumentation markets (let's assume a CAGR of 5% for illustrative purposes), and a speculated 2019 market size of $250 million, the market size in 2025 could be estimated at approximately $320 million. This growth is fueled by the robust expansion of industrial automation and monitoring needs in sectors like manufacturing, environmental monitoring, and laboratories. The rising adoption of sophisticated data logging and analysis techniques, coupled with the ongoing need for reliable, visual representations of continuous process data, further boosts market demand. The proliferation of multi-channel recorders catering to complex monitoring applications is a significant trend. However, the market faces constraints like the increasing adoption of digital data acquisition systems and the high initial investment costs associated with some advanced strip chart recorders. Segmentation by channel count (single, 2, 6, 8, 12, others) and application (industrial/equipment monitoring, environmental monitoring, laboratory, others) provides a granular understanding of market dynamics. The competition is robust, with established players like OMEGA, Yokogawa, and Honeywell competing with specialized manufacturers. The market's future trajectory suggests continued, albeit moderate, growth, propelled by the ongoing need for reliable, visual process data representation in industries that require continuous monitoring. While digital alternatives are gaining traction, the inherent simplicity, reliability, and immediate visual feedback provided by strip chart recorders ensure their continued relevance in specific applications. The geographical distribution of the market is broad, with North America and Europe currently holding significant shares, but emerging economies in Asia Pacific are expected to show promising growth rates in the forecast period (2025-2033), driven by industrialization and infrastructure development. Further segmentation based on specific industries within each application category could reveal even more nuanced growth drivers and market opportunities. The estimated 5% CAGR, while an assumption for illustrative purposes, reflects typical growth patterns in this segment and provides a reasonable basis for projecting future market performance.

These data were cited in the following:

        Marr, J.W. 1967. Data on mountain environments. I. Front
        Range, Colorado, sixteen sites, 1952-1953. University of Colorado Studies,
        Series in Biology 27, 110 pp.

  Marr, J.W., A.W. Johnson, W.S. Osburn, and O.A. Knorr. 1968. Data on 
        mountain environments. II. Front Range, Colorado, four climax regions,
        1953-1958. University of Colorado Studies, Series in Biology 28, 169 pp.
  Marr, J.W., J.M. Clark, W.S. Osburn, and M.W. Paddock. 1968. Data on 
        mountain environments. III. Front Range, Colorado, four climax regions,
        1959-1964. University of Colorado Studies, Series in Biology 29, 181 pp.

  Precipitation data were collected from a ridgetop climate station east of Niwot
      Ridge (B-1 @ 2591 m) throughout the year using a chart recorder. Initially,
      instrumentation consisted of an 8-inch metal rain gauge with the receiving rim about
      3 feet above the ground and measurements were made on an approximately weekly basis.
      More recently, instrumentation consisted of a Fergusson-type weighing rain gauge.
      Precipitation was caught in a bucket containing ethylene glycol (to melt snow) and
      light oil (to prevent evaporation). As the weight of the bucket increased, the pen
      moved up via a spring mechanism and recorded on a rotating chart. Precipitation was
      recorded on a continuous basis. 
 NOTE: The LTER data portal display does not display important maintenance/log
      information or other EML metadata features. Please be sure to view the EML file (a
      text file that contains XML tags) which is included in the zip archive (click on
      "Download zip archive") pertaining to each dataset. The EML file name will have the
      following format: knb-lter-nwt.[3 digit dataset number].[version number].xml. Most
      web browsers can parse the EML so it's easier to read.

The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.

The code has been published on GitHub: Lightning_Detection_Location or DOI 10.5281/zenodo.13350849