First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatio-temporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 years of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

Methods

These files were written by: G. V. DiRenzo

If you have any questions, please email: grace.direnzo@gmail.com

This repository provides the code, data, and simulations to recreate all of the analysis, tables, and figures presented in the manuscript.

In this file, we direct the user to the location of files.

All methods can be found in the manuscript and associated supplements.

All file paths direct the user in navigating the files in this repo.

######## Objective & Table of contents

File objectives & Table of contents:

# 1. To navigate to files explaining how to simulate and analyze data using the main text parameterization
# 2. To navigate to files explaining how to simulate and analyze data using the alternative parameterization (hidden Markov model)
# 3. To navigate to files that created the parameter combinations for the simulation studies
# 4. To navigate to files used to run scenarios in the manuscript
  # 4a. Scenario 1: data generated without site memory & without site heterogenity
  # 4b. Scenario 2: data generated with site memory & without site heterogenity
  # 4c. Scenario 3: data generated with site memory & with site heterogenity
# 5. To navigate to files for general sample design guidelines
# 6. Parameter accuracy, precision, and bias under different parameter combinations
# 7. Model comparison under different scenarios
# 8. To specifically navigate to code that recreates manuscript:
  # 8a. Figures
  # 8b. Tables
# 9. To navigate to files for empirical analysis

### 1. Main text parameterization

To see model parameterization as written in the main text, please navigate to: /MemModel/OtherCode/MemoryMod_main.R

### 2. Alternative parameterization

To see alternative parameterization using a Hidden Markov Model, please navigate to: /MemModel/OtherCode/MemoryMod_HMM.R

### 3. Parameter Combinations

To see how parameter combinations were generated, please navigate to: /MemModel/ParameterCombinations/LHS_parameter_combos.R

To see stored parameter combinations for simulations, please navigate to: /MemModel/ParameterCombinations/parameter_combos_MemModel4.csv

### 4a. Scenario #1

To simulate data WITHOUT memory and analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/withoutMem/Code/ MemoryMod_JobArray_withoutMem.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/withoutMem/ModelOutput

### 4b. Scenario #2

To simulate data WITH memory and analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/withMem/Code/ MemoryMod_JobArray_withMem.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/withMem/ModelOutput

### 4c. Scenario #3

To simulate data WITH memory and WITH site heterogenity- analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/Hetero/Code/ MemoryMod_JobArray_Hetero.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/Hetero/ModelOutput

### 5. General sample design guidelines

To see methods for the general sample design guidelines, please navigate to: /MemModel/PostProcessingCode/Sampling_design_guidelines.R

### 6. Parameter accuracy, precision, and bias under different parameter combinations

To see methods for model performance under different parameter combinations, please navigate to: /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

### 7. Comparison of model performance

To see methods for model comparison, please navigate to: /MemModel/PostProcessingCode/ModelComparison.R

### 8a. Manuscript Figures

To create parts of Figure 1 of main text (case study): - Fig 1D & 1E: /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

To create Figure 2 of main text (Comparison across simulation scenarios): - /MemModel/PostProcessingCode/ModelComparison.R

To create Figure S1, S2, & S3 use file: - /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

To create Figure S4 & S5 use file: - /MemModel/PostProcessingCode/ModelComparison.R

### 8b. Manuscript Tables

To create Table 1 of main text (General sampling recommendations): - /MemModel/PostProcessingCode/Sampling_design_guidelines.R

To create Table S1: - /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

To create Table S2: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

To create Table S3: - /MemModel/PostProcessingCode/ModelComparison.R

To create Table S4 & S5: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

### 9. Empirical analysis

To recreate the empirical analysis of the case study, please navigate to: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

The datasets are part of the study titled "A web-based Delphi multi-criteria group decision-making framework for renewable energy project development processes." The study aims to outline and implement the web-based Delphi Multi-criteria Group Decision Making (Delphi-MGDM) Framework, which is intended to facilitate top-level group decision-making for renewable energy project development and long-term strategic direction setting. The datasets include: (1) the weights of criteria obtained from judgments of the experts, (2) the summary of criteria scores, (3) the comparison table dataset, and (4) the full report of the Visual PROMETHEE. “Criteria Weighing Dataset” is obtained from the judgment of experts using the AHP-Online System created by Klaus D. Goepel (available at https://bpmsg.com/ahp/ahp.php). On a pairwise comparison basis, we asked the experts to make their opinion on four (4) criteria and then the sixteen (16) sub-criteria in three rounds. The group weights after the third round are considered the final weights of criteria and sub-criteria. To rank RES using MCDA, we used the data from the literature and the Philippines’ DOE for all ten quantitative sub-criteria. However, there are six qualitative sub-criteria, so we asked the opinion of experts on how solar, wind, biomass, and hydropower are performing in each criterion based on their knowledge and expertise. This time, we used a self-derived questionnaire and as a summary of this process, we produced the “Scoring of Options Dataset.” We got the average, minimum and maximum values of the scores to make data for the ranking in three cases (realistic, pessimistic, and optimistic). "Comparison table" dataset is composed of comparison tables for the three cases. Table A reflects the data for realistic case in which we use the averages of the qualitative inputs from experts, the averages of quantitative data obtained in ranges, and the actual value of data not given in ranges. Table B reflects the data for the optimistic case. For qualitative data, we used the minimum value of the sub-criteria to be minimized and maximum value for sub-criteria to maximized. For quantitative data in ranges, we used the minimum value of cost sub-criteria and maximum value of benefit sub-criteria. We estimated fictitious data for some quantitative data not given in ranges. Table C reflects the data for the pessimistic case. We used the same concept with Table B, but with opposite choices. For instance, we used the maximum value of cost sub-criteria and minimum value of benefit sub-criteria for quantitative data. Finally, we used Visual PROMETHEE (available at http://www.promethee-gaia.net/vpa.html) to rank renewable energy sources. The "Visual PROMETHEE Full Report" dataset is the actual report exported from the Visual PROMETHEE application – containing a partial and complete ranking of RES.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:

1. Simulation and observational results of meteorological and air quality including four folders:

Day_PBLH: Daily PBLH data

Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO

Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation

Hour_radiation: Hourly surface radiation data

2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:

AOD: Yearly and seasonal AOD data

CF: Yearly and seasonal CF data

CO: Yearly and seasonal CO data

LWP: Yearly and seasonal LWP data

NO2: Yearly and seasonal NO2 data

O3: Yearly and seasonal O3 data

Precipitation: Yearly and seasonal precipitation data

Radiation: Yearly and seasonal radiation data

SO2: Yearly and seasonal SO2 data

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Since the 5-year data do not benefit from data quality filtering, comparisons are only made for populations of 5,000 or more..Geographic areas are based on the geographic boundaries of the data year. Current year comparisons with past-year estimates are not re-tabulated to the current year's geographies; rather, the comparison is with the existing geography of each data year. Statistically significant change from prior years' estimates could be the result of changes in the geographic boundaries of an area and not necessarily the demographic, social, or economic characteristics. For more information on geographic changes, see: https://www.census.gov/programs-surveys/acs/guidance.html.....Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..An * indicates that the estimate is significantly different (at a 90% confidence level) than the estimate from the most current year. A "c" indicates the estimates for that year and the current year are both controlled; a statistical test is not appropriate. A blank indicates that the estimate is not significantly different from the estimate of the most current year, or that a test could not be done because one or both of the estimates is displayed as "-", "N", or "(X)", or the estimate ends with a "+" or "-". (For more information on these symbols, see the Explanation of Symbols below this table.).Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2012-2016 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; or a fixed wireless subscription..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Methodological changes to data collection in 2013 may have affected language data for 2013. Users should be aware of these changes when using 2013 data or multi-year data containing data from 2...

This table contains information on Dutch imports, exports and net balance of services broken down by various service types and countries (groups) provided or purchased by companies and persons domiciled in the Netherlands.

An entirely renewed classification of services which are used by all EU countries from 2014 onwards is based on the “Balance of Payments Manual 6” (BPM6).

Data available from: 2020

Status of the figures: As of 2020, a redesign of the International trade in services has taken place. To make time series and mutual comparison possible, 2020 will be published in 2 ways. In the 2014-2020 time series table 2020 is model-based using response data and auxiliary data from the tax authorities. The first quarter of 2021 has temporarily been added to the old 2014-2020 table awaiting the publication of the new table. With the release of the new table starting in 2020, the first quarter 2021 is no longer published in the old time series.

As of 2017 is the publication and revision policy of international trade in services review. The figures on the Dutch international trade in services are available on quarterly and annual basis. Provisional quarterly figures are one quarter after the end of the reporting period available. With the publication of a new quarter, also the figures for the previous quarter and if applicable the other previous quarters of the year are adjusted on the basis of new source material (provisional figures). At the time of a year four quarters are available quarterly on the basis of these four figures a year calculation is made. This year calculation is available in the autumn of the following year. The year calculation is then adjusted again the following year, this time final. Also the quarterly figures of the year reviewed.

Changes as of December 17, 2024: The provisional figures of the second quarter 2024 have been adjusted and are revised provisional now, also the provisional figures of the third quarter 2024 have been added

Changes as of November 19, 2024: In the previously published data for 2022, the quarterly figures were reported correctly. However, an error occurred when calculating the total for the year, as the June data point was mistakenly used instead of the correct final quarterly figure. As a result, the quarterly figures did not add up to the published annual total. This error has now been corrected, ensuring that the published total aligns with the sum of the individual quarters.

When will new figures be published? The first figures become available approximately 12 weeks after the reporting period.

This is the replication package for the paper "Uncertainty about What's in the Price", to be published in the Journal of Financial Economics.

The data we provide is a subsample of our data files with scrambled company IDs and other identifying information removed. The subsample consists of data for 100 firms selected by, in that order, S&P inclusion/deletion events and data coverage.

The main analysis is run in Stata/MP 15.1 using the +replication do-file. For more information about data source files and data processing, see the Readme file and the +replication do-file.

The following list links the tables in our paper with code lines and output files produced by our main code file “+replication.do”.

Table # Program Line Number Output file Table 1 Panel (a) +replication.do 400-403 table1.rtf Table 1 Panel (b) +replication.do 404-407 table1.rtf Table 2 +replication.do 417-427 table2.rtf Table 3 +replication.do 457-466 table3.rtf Table 4 Panel (a) +replication.do 506-516 table4.rtf Table 4 Panel (b) +replication.do 519-529 table4.rtf Table 4 Panel (c) +replication.do 532-542 table4.rtf Table 4 Panel (d) +replication.do 545-555 table4.rtf Table 5 Panel (a) +replication.do 688-695 table5.rtf Table 5 Panel (b) +replication.do 697-704 table5.rtf Table 6 Panel (a) +replication.do 883-893 table6a.rtf Table 6 Panel (b) +replication.do 1079-1089 table6b.rtf Table 6 Panel (c) +replication.do 564-574 table6c.rtf

Name: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition

Summary: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.

License: cc-BY-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.

Collection Date: 22/07/2022

Publication Date: 01/06/2024

DOI: 10.5281/zenodo.11234441

Other repositories:

Author: University of Deusto

Objective of collection: This data was originally collected to build a set of causal diagrams of the .

Description:

Scenarios: This dataset contains the description of 20 different scenarios used in this research activity.

File 1 - individual reasons to be codedThis dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:

Methodology: Methodology followed by the coders.

Help: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding.

Glossary: Glossary of terms build by the experts coding the answers.

Appliances/Flexibility/Buildings/Mobility: The contributions of each expert, the code provided by the two researchers and the consensus achived.

Summary: Assesment of the results.

File 2 - individual microdata to sort determinants into causal threads from expertsThis dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM.

File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts

This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.

Stage 1: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:

European panels: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)

Latin America panels: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)

Stage 2: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.

Stage3: In the "Archetypes - Consensus Results" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).

Stage 4. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:

Table (s): Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.

Table (t): This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.

Table (t.1): This table shows just the agreements (X) between both context: European & Latin American Archetypes.

Table (t.2): Show the difference between both context: European (E) & Latin American Archetypes (L).

Table (t.3): This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.

5 star: ⭐⭐⭐

Preprocessing steps: Data transcription from written documents and oral discussions.

Reuse: NA

Update policy: No more updates are planned.

Ethics and legal aspects: Names of the persons involved have been removed.

Technical aspects:

Other:

This repository contains all data and code required to reproduce the growth curve-fitting analysis from the manuscript: McMillan, E. A., Berrang, M. E., Read, Q. D., Rasamsetti, S., Richards, A. K., Shariat, N. W., & Frye, J. G. (2022). Buffered peptone water formulation does not influence growth of pESI-positive Salmonella enterica serovar Infantis. Journal of Food Protection, 100033. https://doi.org/10.1016/j.jfp.2022.100033 Manuscript abstract Salmonella enterica is a major cause of human foodborne illness and is often attributed to poultry food sources. S. enterica serovar Infantis, specifically those carrying the pESI plasmid, has become a frequently isolated serotype from poultry meat samples at processing and has caused numerous recent human infections. In 2016, the USDA Food Safety and Inspection Service changed the official sampling method for raw poultry products from BPW to using neutralizing BPW (nBPW) as the rinsing agent in order to prevent residual antimicrobial effects from acidifying and oxidizing processing aids. This change was contemporaneous to the emergence of pESI-positive ser. Infantis as a prevalent serovar in poultry, prompting some to question if nBPW could be selecting for this prevalent serovar. We performed two experiments: a comparison of ser. Infantis growth in BPW versus nBPW, and a simulation of regulatory sampling methods. We found that when inoculated into both broths, ser. Infantis initially grows slightly slower in nBPW than in BPW but little difference was seen in abundance after six hours of growth. Additionally, use of nBPW to simulate poultry rinse sample and overnight cold shipping to a regulatory lab did not affect survival or subsequent growth of ser. Infantis in BPW. We concluded that the change in USDA-FSIS methodology to include nBPW in sampling procedures has likely not affected the emergence of S. ser. Infantis as a prevalent serovar in chicken and turkey meat product samples. Contents All necessary data are in a single comma-separated file, Sal_Infantis_growth_curve_data_EAM.csv. All R code is in a single RMarkdown document, salmonella_growth_curve_fitting.Rmd. The RMarkdown contains code to read and process the data, produce exploratory plots, fit the model, do all hoc calculations with the posterior output, and produce figures and tables from the manuscript. Salmonella Infantis growth data: This is a comma-separated file containing data needed to reproduce the growth curve fitting analysis. Columns are: Strain: numerical ID of strain (see Table 1 in manuscript) Colony_Forming_Units_permL(A, B, C): columns 2-4 are three replicate measurements of colony forming units per mL taken from the same sample at the same time. Media: whether nBPW or BPW was used in the growth medium Time_hours: time in hours ranging from 0-6. RMarkdown document with all analysis code: This RMarkdown document contains code to read and process the data, produce exploratory plots, fit the model, do all hoc calculations with the posterior output, and produce figures and tables from the manuscript. Software versions This was run on Windows 10, R version 4.1.2. Models were fit using CmdStan version 2.28.2, with brms version 2.17.0, cmdstanr version 0.4.0, emmeans version 1.7.3, and tidybayes version 3.0.2. Program information National Program: Food Safety (108) Project Plan Number: 6040-32000-085-000-D Resources in this dataset:Resource Title: Salmonella Infantis growth data. File Name: Sal_Infantis_growth_curve_data_EAM.csvResource Title: RMarkdown document with all analysis code. File Name: salmonella_growth_curve_fitting.Rmd

MODIS product version comparison application for Google Earth Engine This is associated an article published by IEEE in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing on 20 March 2019, available online at doi.org/10.1109/JSTARS.2019.2901404. Reference: Peter, B.G. and Messina, J.P., 2019. Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4), pp.1165-1174. Link to manuscript https://ieeexplore.ieee.org/abstract/document/8672086 Interactive Google Earth Engine Application https://cartoscience.users.earthengine.app/view/versions Google Earth Engine Code // Version 1.1 Map.setCenter(30, 20, 2.5).setOptions('HYBRID').style().set('cursor', 'crosshair'); var countryList = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017'); var stats = function(year) { Map.layers().reset(); var countrySelected = app.country.countrySelect.getValue(); var region = countryList.filterMetadata('Country', 'equals', countrySelected).geometry(); var versionOne = app.inputBox.productBox.getValue(); var versionTwo = app.inputBox.productBoxTwo.getValue(); var band = app.inputBox.bandBox.getValue(); var bandTwo = app.inputBox.bandBoxTwo.getValue(); if (app.inputBox.customCheckbox.getValue() === true) { var latCoord = ee.Number.parse(app.inputBox.latCoordBox.getValue()).getInfo(); var lonCoord = ee.Number.parse(app.inputBox.lonCoordBox.getValue()).getInfo(); var distBuffer = ee.Number.parse(app.inputBox.distBox.getValue()).getInfo(); var distNum = distBuffer*1000; region = ee.Geometry.Point([lonCoord,latCoord]).buffer(distNum).bounds(); } var modisCollectionOne = ee.ImageCollection(versionOne).select(band); var modisCollectionTwo = ee.ImageCollection(versionTwo).select(bandTwo); var imageOne = modisCollectionOne.filter(ee.Filter.calendarRange(year,year,'year')).mean(); var imageTwo = modisCollectionTwo.filter(ee.Filter.calendarRange(year,year,'year')).mean(); var abs = imageOne.select(band).subtract(imageTwo.select(bandTwo)).abs().rename("difference"); var percentilesOne = imageOne.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var percentilesTwo = imageTwo.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var percentilesAbs = abs.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var minOne = ee.Number(percentilesOne.get(band+'_p10')).getInfo(); var maxOne = ee.Number(percentilesOne.get(band+'_p90')).getInfo(); var minTwo = ee.Number(percentilesTwo.get(bandTwo+'_p10')).getInfo(); var maxTwo = ee.Number(percentilesTwo.get(bandTwo+'_p90')).getInfo(); var minBoth = Math.min(minOne,minTwo); var maxBoth = Math.max(maxOne,maxTwo); var minAbs = ee.Number(percentilesAbs.get('difference_p10')).getInfo(); var maxAbs = ee.Number(percentilesAbs.get('difference_p90')).getInfo(); var grayscale = ['f7f7f7', 'cccccc', '969696', '525252','141414']; Map.addLayer(imageOne.select(band).rename(band+'_'+versionOne).clip(region),{min: minBoth, max: maxBoth, palette: grayscale},band+' • '+versionOne, false); Map.addLayer(imageTwo.select(bandTwo).rename(bandTwo+'_'+versionTwo).clip(region),{min: minBoth, max: maxBoth, palette: grayscale},band+' • '+versionTwo, false); Map.addLayer(abs.clip(region),{min: minAbs, max: maxAbs, palette: grayscale},"Difference"); var options = { title: year+' Histogram', fontSize: 11, legend: {position: 'none'}, series: {0: {color: '7100AA'}} }; var histogram = ui.Chart.image.histogram(imageOne, region, 10000).setOptions(options); var optionsTwo = { title: year+' Histogram', fontSize: 11, legend: {position: 'none'}, series: {0: {color: '0071AA'}} }; var histogramTwo = ui.Chart.image.histogram(imageTwo, region, 10000).setOptions(optionsTwo); var clickLabel = ui.Label('Click map to get pixel time-series', {fontWeight: '300', fontSize: '13px', margin: '10px 10px 15px 30px'}); var clickLabelTwo = ui.Label('Click map to get pixel time-series', {fontWeight: '300', fontSize: '13px', margin: '10px 10px 15px 30px'}); app.rootPanels.panelOne.widgets().set(1, ui.Label('temp')); app.rootPanels.panelTwo.widgets().set(1, ui.Label('temp')); app.rootPanels.panelOne.widgets().set(1, histogram); app.rootPanels.panelOne.widgets().set(2, clickLabel); app.rootPanels.panelTwo.widgets().set(1, histogramTwo); app.rootPanels.panelTwo.widgets().set(2, clickLabelTwo); Map.centerObject(region); Map.setOptions('HYBRID'); Map.onClick(function(coords) { var point = ee.Geometry.Point(coords.lon, coords.lat); var dot = ui.Map.Layer(point, {color: 'AA0000'}, "Inspector"); Map.layers().set(3, dot); var clickChart = ui.Chart.image.series(modisCollectionOne, point, ee.Reducer.mean(), 10000); clickChart.setOptions({ title: 'Pixel | X: ' + coords.lon.toFixed(2)+', '+'Y: ' + coords.lat.toFixed(2),...

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.