Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

This dataset is about books. It has 3 rows and is filtered where the book is Statistical analysis with Excel for dummies. It features 7 columns including author, publication date, language, and book publisher.

The Excel files correspond to the raw ELISpot assay data regarding the BCN02 clinical trial.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size.2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.5632773. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002andBrown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

This folder contains the material of an experiment that compares MDD and Ccd in Unreal Engine Context.

The material in this folder* is organized in three subfolders: 01_EXPERIMENT, which contains the information used to run the experiment; 02_RESULTS, which contains the information collected in the experiment; and 03_STATISTICAL ANALYSIS, which contains the data set and the results of the statistical analysis performed.

The documentation contained in each of these folders is described below:

01_EXPERIMENT

11_ CdE_ApproveReport: pdf file of the Favorable report from the ethical and scientific committee regarding the execution of the experiment.

12_Forms&Exercises: This folder contains the Taks Sheet for each group and the Unreal project with each exercise to resolve.

13_SupportMaterial: This folder contains the support material that the subjects could use during the performance of the different tasks of the experiment.

14_SessionMaterial: This folder contains the material used by the instructors during the session, including the video tutorial.

15_CorrectionMaterial: This folder contains the solution to the tasks and the rubric to correct.

02_RESULTS

201_DATASET: Data files containing the values of variables and factors necessary for conducting the statistical analysis proposed in the study. They are included in both IBM SPSS Statistics (.sav) and Microsoft Excel (.xlsx) formats.

202_BOXPLOTS-HISTOGRAMS: Files that contain the execution and results of the boxplots for all the variables and factors considered. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

203_DATASET_LMM: Files that contain the dataset that includes the residuals of LMM executions. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

202_DEMOGRAPHIC: Excel file with the demographic results.

203_DESCRIPTIVES: Files that contain the values of the main descriptive statistics and the results of the normality tests that correspond to all the variables measured in the study: the response variables and the independent variables or factors. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

204_LMM: Files that contain the execution and results of the LMM Type III test of fixed effects with unstructured repeated covariance for all the variables in the study with different statistical models. The files also includes the normality test of the residuals of LMM executions. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

205_LMM_model selection: File that contain information of the statistical models used in the statistical analysis and the comparative.

206_ DESSCRIPTIVES COHEN: Files that include the computations executed to determine the effect size of the factors in all the dependent variables. It is are included in (.exe) format.

*Note: The documentation that appears in this folder contains texts in Spanish, since this is the language in which the experiment is executed.

This statistic displays the market research tools most used by professionals in the market research industry in the United States in 2017 and 2018. During the 2018 the survey, ** percent of respondents stated they usef Microsoft Excel, compared to ** percent in the 2017 survey.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 2.1 protection status for various land unit boundaries (Protected Areas Database of the United States (PAD-US) Summary Statistics by GAP Status Code) as well as summaries of public access status (Public Access Statistics), provided in Microsoft Excel readable workbooks, the vector GIS analysis files and scripts used to complete the summaries, and raster GIS analysis files for combination with other raster data. The PAD-US 2.1 Combined Fee, Designation, Easement feature class in the full inventory (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

This excel contains results from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 4: "Population." The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990, 2000, and 2010 10m cell population density rasters were produced using Rhode Island state land use data, Massachusetts state land use, Connecticut NLCD land use data, and U.S. Census data. To generate a population estimate (number of persons) for any given area within the boundaries of this raster, NBEP used the the Zonal Statistics as Table tool to sum the 10m cell density values within a given zone dataset (e.g., watershed polygon layer). Results presented include population estimates (1990, 2000, 2010) as well as calculation of percent change (1990-2000;2000-2010;1990-2010).

Objectives: To examine trends in strong opioid prescribing in a primary care population in Wales and identify if factors such as age, deprivation and recorded diagnosis of depression or anxiety may have influenced any changes noted.

Design: Trend, cross-sectional and longitudinal analyses of routine data from the Primary Care General Practice database and accessed via the Secure Anonymised Information Linkage (SAIL) databank. Setting: A total of 345 Primary Care practices in Wales.

Participants: Anonymised records of 1,223,503 people aged 18 or over, receiving at least one opioid pre- scription between 1 January 2005 and 31 December 2015 were analysed. People with a cancer diagnosis (10.1%) were excluded from the detailed analysis.

Results: During the study period, 26,180,200 opioid prescriptions were issued to 1,223,503 individuals (55.9% female, 89.9% non-cancer diagnoses). The greatest increase in annual prescribing was in the 18–24 age group (10,470%), from 0.08 to 8.3 prescriptions/1000 population, although the 85+ age group had the highest prescribing rates across the study period (from 149.9 to 288.5 prescriptions/1000 popu- lation). The number of people with recorded diagnoses of depression or anxiety and prescribed strong opioids increased from 1.2 to 5.1 people/1000 population (328%). The increase was 366.9% in areas of highest deprivation compared to 310.3 in the least. Areas of greatest deprivation had more than twice the rate of strong opioid prescribing than the least deprived areas of Wales.

Conclusion: The study highlights a large increase in strong opioid prescribing for non-cancer pain, in Wales between 2005 and 2015. Population groups of interest include the youngest and oldest adult age groups and people with depression or anxiety particularly if living in the most deprived communities. Based on this evidence, development of a Welsh national guidance on safe and rational prescribing of opioids in chronic pain would be advisable to prevent further escalation of these medicines.

Methods Data extracted from the Secure Anonymised Information Linkage databank (SAIL). SQL code used to extract annualised totals for each subset of data.

Excel and SPSS25 used to analyse data using descriptive statistical methods.

Excel used to produce trend graphs and totals.

This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:

The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.

The relationships between trait plasticity and canalization, and between phenotypic integration and plasticity, have been under debate, largely because direct evidence is still scarce for their associations, especially in response to environments. To investigate the relationships between canalization, integration, and phenotypic plasticity in response to emergence time, we conducted a field experiment with an annual herbaceous species of Abutilon theophrasti, by subjecting plants to four treatments of emergence time (spring, late spring, summer, and late summer), to measure several morphological traits and analyze correlations of plasticity with canalization and integration in these traits, at two stages of plant growth. Results showed plants with delayed emergence had higher phenotypic integration and more positive correlations between integration and plasticity, but less negative correlations between decreased canalization and plasticity, compared to those that emerged in spring. Resu..., Experimental design We conducted the field experiment in 2007 at the Pasture Ecological Research Station of Northeast Normal University, Changling, Jilin Province, China (44°45’ N, 123°45’ E). The original soil of the experimental field (aeolian sandy soil, pH = 8.3) at the station had been used annually for many years, with nutrients availability of organic C 3.1 mg kg–1, available N 21.0 mg kg–1, and available P 1.1 mg kg–1 during the growth season of 2007. Seeds of A. theophrasti were collected from local wild populations near the research station in late August 2006 and dry stored at -4°C. We applied a randomized block design, with emergence timing (ET) as the main factor, and block as the sub-factor. The whole plot was divided into twelve 2 × 3 m sub-plots, which were randomly assigned with four ET treatments and three blocks. Plants of A. theophrasti were grown on June 7, June 27, July 17, and August 7, to make them emerge in different periods of the season, as four ET treatments ..., , # Morphological integration, canalization, and plasticity in response to emergence time in Abutilon theophrasti

https://doi.org/10.5061/dryad.ncjsxksx1

This dataset contains one Excel file with three sheets.

The dataset for the article "The current utilization status of wearable devices in clinical research".Analyses were performed by utilizing the JMP Pro 16.10, Microsoft Excel for Mac version 16 (Microsoft).The file extension "jrp" is a file of the statistical analysis software JMP, which contains both the analysis code and the data set.In case JMP is not available, a "csv" file as a data set and JMP script, the analysis code, are prepared in "rtf" format.The "xlsx" file is a Microsoft Excel file that contains the data set and the data plotted or tabulated using Microsoft Excel functions.Supplementary Figure 1. NCT number duplication frequencyIncludes Excel file used to create the figure (Supplemental Figure 1).・Sfig1_NCT number duplication frequency.xlsxSupplementary Figure 2-5　Simple and annual time series aggregationIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 2-5).・Sfig2-5 Annual time series aggregation.xlsx・Sfig2 Study Type.jrp・Sfig4device type.jrp・Sfig3 Interventions Type.jrp・Sfig5Conditions type.jrp・Sfig2, 3 ,5_database.csv・Sfig2_JMP script_Study type.rtf・Sfig3_JMP script Interventions type.rtf・Sfig5_JMP script Conditions type.rtf・Sfig4_dataset.csv・Sfig4_JMP script_device type.rtfSupplementary Figures 6-11 Mosaic diagram of intervention by conditionSupplementary tables 4-9 Analysis of contingency table for intervention by condition JMP repot files used to create the figures(Supplementary Figures 6-11 ) and tables(Supplementary Tablea 4-9) , including the csv dataset of JMP repot files and JMP scripts.・Sfig6-11 Stable4-9 Intervention devicetype_conditions.jrp・Sfig6-11_Stable4-9_dataset.csv・Sfig6-11_Stable4-9_JMP script.rtfSupplementary Figure 12. Distribution of enrollmentIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 12).・Sfig12_Distribution of enrollment.jrp・Sfig12_Distribution of enrollment.csv・Sfig12_JMP script.rtf

An excel table with grainsize distribution data and statistics, in micrometres and phi. 13 samples. Data measured with laser diffraction.

Posttranslational modifications of epigenetic modifiers provide a flexible and timely mechanism for rapid adaptations to the dynamic environment of cancer cells. SIRT1 is an NAD+-dependent epigenetic modifier whose activity is classically associated with healthy aging and longevity, but its function in cancer is not well understood. Here, we reveal that 1a,25-dihydroxyvitamin D3 (1,25(OH)2D3, calcitriol), the active metabolite of vitamin D (VD), promotes SIRT1 activation through auto-deacetylation in human colon carcinoma cells, and identify lysine 610 as an essential driver of SIRT1 activity. Remarkably, our data show that the post-translational control of SIRT1 activity mediates the antiproliferative action of 1,25(OH)2D3. This effect is reproduced by the SIRT1 activator SRT1720, suggesting that SIRT1 activators may offer new therapeutic possibilities for colon cancer patients who are VD deficient or unresponsive. Moreover, this might be extrapolated to inflammation and other VD defic..., For Immunofluorescences, datasets were collected with ImageJ software as fluorescence intensity corrected by cell number. For each experiment (named in the excel n1 to nx), 3 different fields were evaluated per slide. Statistical methods are detailed in the Excel file. For western blots, bands were measured by densitometry and analyzed with ImageJ software. Statistical methods are detailed in the Excel file. For Deacetylase assays and NAD+ levels, datasets were collected from the luminometer. Statistical methods are detailed in the Excel file. For TMAs in Figure 3C-E: immunoreactivity was quantified blind with a Histoscore (H score) that considers both the intensity and percentage of cells stained for each intensity (low, medium, or high) following this algorithm (range 0–300): H score = (low%) × 1 + (medium%) × 2 + (high %) × 3. Quantification for each patient biopsy was calculated blindly by 2 investigators. Statistical analysis was performed with Chi-square test. For the TNM plots, d..., Datasets are presented in Excel. For TNM plots we used this link: https://tnmplot.com/analysis/ Rawdata: original data are presented in PowerPoint No additional software is required.

These four Mathematica files present the method of damage localization by statistical methods along with the determination of the extent of damage by the Tikhonov regularization. The results are used in the lone excel file. The results could be reproduced by the Mathematica files.