Students use small mammal data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by re-organising excel sheets in an R-compatible format and doing basic analysis in R

General description

This dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.

1. Is the research article an Open Access publication?

2. Does the research article have a Creative Common license or a similar license?

3. Does the research article contain a data availability statement?

4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?

5. Does the research article contain supplementary data?

6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?

Variables

The data were compiled in a Microsoft Excel 365 document that includes the following variables.

1. DOI URL of research article

2. Year of publication

3. Research article published with Open Access

4. License for research article

5. Data availability statement in article

6. Supplementary data added to article

7. Persistent identifier for supplementary data

8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDC

Visualization

Parts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).

File formats and software

The file formats used in this dataset are:

.csv (Text file) .docx (Microsoft Word 365 file) .jpg (JPEG image file) .pdf/A (Portable Document Format for archiving) .png (Portable Network Graphics image file) .pptx (Microsoft Power Point 365 file) .txt (Text file) .xlsx (Microsoft Excel 365 file)

All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016.

MD5 checksums

Here is a list of all files of this dataset and of their MD5 checksums.

1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)
2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)
3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),
4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),
5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),
6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),
7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),
8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),
9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),
10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),
11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),
12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),
13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),
14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),
15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),
16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),
17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),
18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),
19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

These datatasets relate to the computational study presented in the paper The Berth Allocation Problem with Channel Restrictions, authored by Paul Corry and Christian Bierwirth. They consist of all the randomly generated problem instances along with the computational results presented in the paper.

Results across all problem instances assume ship separation parameters of [delta_1, delta_2, delta_3] = [0.25, 0, 0.5].

Excel Workbook Organisation:

The data is organised into separate Excel files for each table in the paper, as indicated by the file description. Within each file, each row of data presented (aggregating 10 replications) in the corrsponding table is captured in two worksheets, one with the problem instance data, and the other with generated solution data obtained from several solution methods (described in the paper). For example, row 3 of Tab. 2, will have data for 10 problem instances on worksheet T2R3, and corresponding solution data on T2R3X.

Problem Instance Data Format:

On each problem instance worksheet (e.g. T2R3), each row of data corresponds to a different problem instance, and there are 10 replications on each worksheet.

The first column provides a replication identifier which is referenced on the corresponding solution worksheet (e.g. T2R3X).

Following this, there are n*(2c+1) columns (n = number of ships, c = number of channel segmenets) with headers p(i)_(j).(k)., where i references the operation (channel transit/berth visit) id, j references the ship id, and k references the index of the operation within the ship. All indexing starts at 0. These columns define the transit or dwell times on each segment. A value of -1 indicates a segment on which a berth allocation must be applied, and hence the dwell time is unkown.

There are then a further n columns with headers r(j), defining the release times of each ship.

For ChSP problems, there are a final n colums with headers b(j), defining the berth to be visited by each ship. ChSP problems with fixed berth sequencing enforced have an additional n columns with headers toa(j), indicating the order in which ship j sits within its berth sequence. For BAP-CR problems, these columnns are not present, but replaced by n*m columns (m = number of berths) with headers p(j).(b) defining the berth processing time of ship j if allocated to berth b.

Solution Data Format:

Each row of data corresponds to a different solution.

Column A references the replication identifier (from the corresponding instance worksheet) that the soluion refers to.

Column B defines the algorithm that was used to generate the solution.

Column C shows the objective function value (total waiting and excess handling time) obtained.

Column D shows the CPU time consumed in generating the solution, rounded to the nearest second.

Column E shows the optimality gap as a proportion. A value of -1 or an empty value indicates that optimality gap is unknown.

From column F onwards, there are are n*(2c+1) columns with the previously described p(i)_(j).(k). headers. The values in these columns define the entry times at each segment.

For BAP-CR problems only, following this there are a further 2n columns. For each ship j, there will be columns titled b(j) and p.b(j) defining the berth that was allocated to ship j, and the processing time on that berth respectively.

This data was downloaded from the Census Hard To County online mapping application. It is the Census Bureau's 2019 Planning Database by Census Tract. The data is being used to identify Hard To Count areas in Delaware that need focus during the 2020 Census collection. The data was an excel spreadsheet titled: pdb2019trev3_us.xls. The Delaware data was extracted from that dataset and split into 2 excel spreadsheets - one with actual numbers and the second with the percentages. This split was done becuase there were too many attributes in the data. These excel sheets were then joined to Census Tract geometry.The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,..., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

The series consists of a list of files registered on the computer-based Records and Correspondence Management System (RCMS), under Registry 01 Corporate Management Division. It was created by exporting file data from the RCMS system into a Microsoft Excel spreadsheet. It is an artificial series, created by the Department of Justice at the request of PROV, to provide access to VPRS 12607 General Correspondence Files, Registry 01 Corporate Management Division.

The list captured the file number, key-term classification, file title, and certain additional information for each file.

Organisation of the Data:

The data is organised into 13 columns, or fields, presumably corresponding to discrete fields within the RCMS database.

The columns, from left to right, are as follows:

1. FILE.YEAR - The year the file was raised.

2. REGISTRY - The number of the registry in which the file has been registered on the RCMS system. The files referred to by this series were registered under Registry 01 Corporate Management Division.

3. FILE SEQUENCE - The sequential number allocated to each file as it is raised. Numbers start again from one each year.

4. FILE PART - The part number of the file.

The FILE.YEAR, REGISTRY, FILE SEQUENCE, and FILE PART fields, taken together, provide the file number.

5. KEY TERM - In theory, this is term used to describe the principle subject area of the file.

6. DESCRIPTOR.1, DESCRIPTOR.2 and DESCRIPTOR.3 (Columns 6 to 8) - In theory, these are narrower terms used to break the general subject area into smaller, more specific areas.

7. KWOC.1, KWOC.2, KWOC.3, and KWOC.4 (Key Word Out of Context) (Columns 9 to 12) - Provide for free text description of the file.

The KEY-TERM, DESCRIPTOR, and KWOC fields, taken together, provide the file title.

In practice, many different terms have been used in the key-term and descriptor fields. There appears to have been little control over the creation of new terms and the way in which the terms are used.

8. ADD.FILE.INFO (Additional File Information) - This field contains useful information about previous and subsequent files, related files, file closure, and so forth.

Identifying Top-numbered Files:

This series also records the original file numbers for files that have been top-numbered into VPRS 12607 from other correspondence registries that operated in the Law Department in the 1980's. The details are as follows:

Files top-numbered from the Central Correspondence Registry (VPRS 266 Inward Registered Correspondence 1857-1986) - the original file number is recorded in the field "ADD.FILE.INFO".

Files top-numbered from the Courts Management Division Registry (VPRS 12705 General Correspondence Files, Courts Management Division) - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Files top-numbered from the Buildings and Property Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from the Human Resource Management Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from RCMS Registry 02 Courts and Tribunals Division - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Researchers should not discount the possibility that file numbers may be recorded in fields other than those specified above.

This file is an Excel 2010 companion and support file to a research manuscript entitled "Computational methodology for quantifying regional and global temperature changes caused by multiple concurrent processes". This file contains multiple tables for the collection of personal expert data and physical process data.

Excel Application Tool for Agricultural Water Use Data 1998 - 2005 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 1998 – 2005 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using California Ag Water Use model for updating the information in the California Water Plan Updates-2003 & 2009. Therefore, this current Excel application tool just covers agricultural water use data from the period of 1998 - 2005 water years. It should also be mentioned that there are 2 other similar Excel application tools that cover 2006 - 2010 and 2011 - 2015 agricultural water use data for the California Water plan Updates - 2013 and 2018, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 1998 – 2005 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu.
Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets were included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3. Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.

4. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

5. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

6. Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

7. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

8. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

9. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

10. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

11. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

12. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

13. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

Data set in Excel. (XLSX 18Â kb)

AASG Wells Data for the EGS Test Site Planning and Analysis Task Temperature measurement data obtained from boreholes for the Association of American State Geologists (AASG) geothermal data project. Typically bottomhole temperatures are recorded from log headers, and this information is provided through a borehole temperature observation service for each state. Service includes header records, well logs, temperature measurements, and other information for each borehole. Information presented in Geothermal Prospector was derived from data aggregated from the borehole temperature observations for all states. For each observation, the given well location was recorded and the best available well identifier (name), temperature and depth were chosen. The "Well Name Source," "Temp. Type" and "Depth Type" attributes indicate the field used from the original service. This data was then cleaned and converted to consistent units. The accuracy of the observation's location, name, temperature or depth was note assessed beyond that originally provided by the service.

• AASG bottom hole temperature datasets were downloaded from repository.usgin.org between the dates of May 16th and May 24th, 2013.
• Datasets were cleaned to remove null and non-real entries, and data converted into consistent units across all datasets
• Methodology for selecting best temperature and depth attributes from column headers in AASG BHT Data sets:

Temperature: CorrectedTemperature - best MeasuredTemperature - next best Depth: DepthOfMeasurement - best TrueVerticalDepth - next best DrillerTotalDepth - last option Well Name/Identifier: APINo - best WellName - next best ObservationURI - last option

The column headers are as follows: gid = internal unique ID src_state = the state from which the well was downloaded (note: the low temperature wells in Idaho are coded as "ID_LowTemp", while all other wells are simply the two character state abbreviation) source_url = the url for the source WFS service or Excel file temp_c = "best" temperature in Celsius temp_type = indicates whether temp_c comes from the corrected or measured temperature header column in the source document depth_m = "best" depth in meters depth_type = indicates whether depth_m comes from the measured, true vertical, or driller total depth header column in the source document well_name = "best" well name or ID name_src = indicates whether well_name came from apino, wellname, or observationuri header column in the source document lat_wgs84 = latitude in wgs84 lon_wgs84 = longitude in wgs84 state = state in which the point is located county = county in which the point is located AASG Wells Data for the EGS Test Site Planning and Analysis Task Temperature measurement data obtained from boreholes for the Association of American State Geologists (AASG) geothermal data project. Typically bottomhole temperatures are recorded from log headers, and this information is provided through a borehole temperature observation service for each state. Service includes header records, well logs, temperature measurements, and other information for each borehole. Information presented in Geothermal Prospector was derived from data aggregated from the borehole temperature observations for all states. For each observation, the given well location was recorded and the best available well identified (name), temperature and depth were chosen. The “Well Name Source,” “Temp. Type” and “Depth Type” attributes indicate the field used from the original service. This data was then cleaned and converted to consistent units. The accuracy of the observation’s location, name, temperature or depth was note assessed beyond that originally provided by the service.

• AASG bottom hole temperature datasets were downloaded from repository.usgin.org between the dates of May 16th and May 24th, 2013.
• Datasets were cleaned to remove “null” and non-real entries, and data converted into consistent units across all datasets
• Methodology for selecting ”best” temperature and depth attributes from column headers in AASG BHT Data sets:

• Temperature: • CorrectedTemperature – best • MeasuredTemperature – next best • Depth: • DepthOfMeasurement – best • TrueVerticalDepth – next best • DrillerTotalDepth – last option • Well Name/Identifier • APINo – best • WellName – next best • ObservationURI - last option.

The column headers are as follows:

• gid = internal unique ID

• src_state = the state from which the well was downloaded (note: the low temperature wells in Idaho are coded as “ID_LowTemp”, while all other wells are simply the two character state abbreviation)

• source_url = the url for the source WFS service or Excel file

• temp_c = “best” temperature in Celsius

• temp_type = indicates whether temp_c comes from the corrected or measured temperature header column in the source document

• depth_m = “best” depth in meters

• depth_type = indicates whether depth_m comes from the measured, true vertical, or driller total depth header column in the source document

• well_name = “best” well name or ID

• name_src = indicates whether well_name came from apino, wellname, or observationuri header column in the source document

• lat_wgs84 = latitude in wgs84

• lon_wgs84 = longitude in wgs84

• state = state in which the point is located

• county = county in which the point is located

NOTE FOR USERS: For local-level projections, such as at a township and municipal-level, please use the original “2018 Series”. This is the data CMAP recommends be used for planning, grant applications, and other official purposes. CMAP is confident in the updated regional-level population projections; however, the projections for township and municipal level populations appear less reflective of current trends in nearterm population growth. Further refinements of the local forecasts are likely needed.CONTENTS:Filename: ONTO2050OriginalForecastData2018.zipTitle: Socioeconomic Forecast Data, 2018 SeriesThis .zip file contains data associated with the original ON TO 2050 forecast, adopted in October 2018. Includes:Excel file of regional projections of population and employment to the year 2050:CMAP_RegionalReferenceForecast_2015adj.xlsx (94kb)Excel file of local (county, municipality, Chicago community area) projections of household population and employment to the year 2050: ONTO2050LAAresults20181010.xlsx (291kb)GIS shapefile of projected local area allocations to the year 2050 by Local Allocation Zone (LAZ): CMAP_ONTO2050_ForecastByLAZ_20181010.shp (19.7mb)Filename: ONTO2050OriginalForecastDocumentation2018.zipTitle: Socioeconomic Forecast Documentation, 2018 SeriesThis .zip file contains PDF documentation of the original ON TO 2050 forecast, adopted in October 2018. Includes:Louis Berger forecast technical report (2016): CMAPSocioeconomicForecastFinal-Report04Nov2016.pdf (2.3mb)Louis Berger addendum (2017): CMAPSocioeconomicForecastRevisionAddendum20Jun2017.pdf (0.6mb)ON TO 2050 Forecast appendix (2018): ONTO2050appendixSocioeconomicForecast10Oct2018.pdf (2.6mb)Filename: Socioeconomic-Forecast-Appendix-Final-October-2022.pdfTitle: Socioeconomic Forecast Appendix, 2022 SeriesDocumentation & results for the updated socioeconomic forecast accompanying the ON TO 2050 plan update, adopted October 2022. PDF, 2.7mbFilename: RegionalDemographicForecast_TechnicalReport_202210.pdfTitle: 2050 Regional Demographic Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional demographic forecast, developed in coordination with the Applied Population Lab at the University of Wisconsin, Madison. PDF, 1.7mbFilename: RegionalEmpForecast_TechnicalReport_202112.pdfTitle: 2050 Regional Employment Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional employment forecast, developed by EBP and Moody's Analytics. PDF, 0.8mbFilename: CMAPRegionalForecastONTO2050update202209.xlsxTitle: Regional Projections, 2022 SeriesProjections of population and employment to the year 2050, produced for the ON TO 2050 plan update adopted October 2022. 60kbFilename: CMAPLocalForecastONTO2050update202210.xlsxTitle: County and Municipal Projections, October 2022 (2022 Series)Projections of population and employment to the year 2050 at the county and municipal level, produced for the ON TO 2050 plan update adopted October 2022.

Excel Application Tool for Agricultural Water Use Data 2006 - 2010 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2006 – 2010 agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using California Ag Water Use model for updating the information in the California Water Plan Updates-2013. Therefore, this current Excel application just covers agricultural water use data from the period of 2006 -2010 water years. It should also be mentioned that there are 2 other similar Excel application tools that cover 1998 - 2005 and 2011 - 2015 agricultural water use data for the California Water plan Updates - 2003/2009 and 2018, respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2006 – 2010 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised. Following are definitions of terminology and listing of 20 crop categories used in this Excel application. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres) Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988). Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year. Crop category numbers and descriptions Crop Category Crop category description. 1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay) 2 Rice (rice, rice_wild, rice_flooded, rice-upland) 3 Cotton 4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early) 5 Corn 6 Dry beans 7 Safflower 8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane 9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual) 10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue) 11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc) 12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc) 13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon) 14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic) 15 Potatoes (potatoes, potatoes_sweet) 16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower) 17 Almond & pistachios 18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis) 19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba) 20 Vineyards (grape_table, grape_raisin, grape_wine)

This dataset contains an ESRI shapefile of all the community planning boundaries of each planning section in the City of Toronto. Manager's name and telephone number is available for each associated planning section. In addition, an excel spreadsheet is available that contains all the community planner's name and telephone number.

Excel spreadsheet of data and codes from articles.

Excel file raw data for publication. Visit https://dataone.org/datasets/sha256%3A02c0d9f0e295cc8aa300f7f65ba6fb4bba780829eea2f8d3836cafa7eba610b0 for complete metadata about this dataset.

Excel Workbook of all tables in the San Francisco Planning Department's Commerce & Industry Inventory 2011.Go here for the document (http://www.sf-planning.org/modules/showdocument.aspx?documentid=8937) or here for all documents (http://www.sf-planning.org/index.aspx?page=1663#commerce_industry).

[Metadata] A database was developed in support of the conventional hydropower assessment that included more than 50 site-specific economic and environmental/social criteria. All of the data collected, along with the original source references, is also available as an electronic Excel spreadsheet that can be sorted depending on criteria. This data is cross-referenced as a GIS shapefile, which is available from the Honolulu USACE. The geospatial data allows users to geographically and visually sort projects based on any of the selected fields including island, size, scale, incremental energy cost, location, etc. The information provided allows the USACE, State, County, and private developers to analyze potential hydropower sites based on their needs and interests. This information is available as Appendix A, and provides complete site descriptions, additional caveats, and details about the original documents describing the sites. Please note that the geospatial locations of existing and proposed hydropower plants varying in accuracy. Certain sites are placed on known XY coordinate locations (e.g. existing powerplants) while others simply make reference to a certain valley or area on the island. Please review the 'Source of Geospatial Data' attribute column for more information. Please note that user-friendly aliases are available in the attribute table when opening the MXDs in this data package. These column aliases provide more information on the data entries as well as units of measurement. The user is also referred to Appendix A of the Technical Appendix which presents this database in an accessible MS Excel format.

Transition visions Heat (TVWs) are municipal policy documents to achieve a natural gas-free heat supply. All municipalities had to have the TVWs available by the end of 2021. The TVWs contain a vision and possible solution directions for the intended heat transition in a municipality. The TransitionVisionWarmte layer contains the data from the Excel file (tables) and the planning areas of the TVW database 2022. It is a combination of Excel data with geometries (Municipal + Document + Plan + Plan area geometries). On the website ‘Database transition vision heat’ of the Expertisecentrum Warme (under theme ‘Region and organisation’), the database can be downloaded as an Excel file. More background information can also be consulted via this website.