Data Dictionary template for Tempe Open Data.

This template covers section 2.5 Resource Fields: Entity and Attribute Information of the Data Discovery Form cited in the Open Data DC Handbook (2022). It completes documentation elements that are required for publication. Each field column (attribute) in the dataset needs a description clarifying the contents of the column. Data originators are encouraged to enter the code values (domains) of the column to help end-users translate the contents of the column where needed, especially when lookup tables do not exist.

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The Master Address Repository (MAR) 2.0 is the successor to the Master Address Repository. The Master Address Repository is a complex and widely accessed database that is increasingly being accessed by many DC Government applications. It is important to have high quality documentation readily accessible for such widely used databases. This document contains the column (field) definitions for the most important views, tables and feature classes within the MAR 2.0.

The Master Address Repository (MAR) is a complex and widely accessed database that is increasingly being accessed by many DC Government applications. For such a widely used databases, it is important to have high quality documentation readily accessible. As a result, this document contains the table definitions for the most important views, tables and feature classes within the MAR.

Programmatically generated Data Dictionary document detailing the TxDOT DCIS All Projects service.

    The PDF contains service metadata and a complete list of data fields.
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Supplementary_Data_Dictionary_Sheet_v1.0.xls

The data dictionary Excel sheet is the main supporting document for the paper.

DD_-_Neonatal_Data.csv

The patient dataset is provided as a format for capturing data with respect to data dictionary.

Programmatically generated Data Dictionary document detailing the Texas Councils of Governments service.

    The PDF contains service metadata and a complete list of data fields.
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The GS:SFHS Data Dictionary is a set of information describing the contents, format, and structure of the phenotype data collected during recruitment (2006-2011) to the Generation Scotland Scottish Family Health Study (GS:SFHS), or derived subsequently from study data collected during recruitment. This dataset replaces the one at https://datashare.is.ed.ac.uk/handle/10283/2724

Programmatically generated Data Dictionary document detailing the TxDOT Roadbed Base service.

    The PDF contains service metadata and a complete list of data fields.
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Encodings types supported by the BinaryCIF format.

Programmatically generated Data Dictionary document detailing the Texas Education Boundaries service.

    The PDF contains service metadata and a complete list of data fields.
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Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Hunter Asset Database v2.4 supersedes previous versions of the Hunter Asset database. In this V2.4 database: (1) Updated M2 test results for 86 assets from the external review (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review (3) Created Draft Water Dependent Asset Register file using the template V5 This dataset contains the Asset database (.mdb), a Geodatabase version for GIS mapping purposes (.gdb), the draft Water Dependent Asset Register spreadsheet, a data dictionary document, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " HUN_asset_database_doc_20151120.doc", located in this filet. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document " HUN_asset_database_doc_20151120.doc" located in this file. Some of the source data used in the compilation of this dataset is restricted. Dataset History OBJECTID VersionID Notes Date_ 1 1 Initial database. 29/08/2014 3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014 4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015 5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015 6 1.4 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) (3) Turn off new GW point asset (AID: 0) (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that NAM assets. (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets. (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database (7)The databases, especially spatial database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the spatial data" 16/06/2015 7 2 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team (2) Added 104 EPBC assets, which were assessed and excluded by ERIN (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets (5) Updated M2 test results (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names may not match with original names in Namoi asset database. (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW WSP assets. It should NOT use for other subregions. (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 20/07/2015 8 2.1 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name and ListDate are different and changed) (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs: 8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771, 60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)" 27/08/2015 9 2.2 "(1) Updated M2 results from the internal review for 386 Sociocultural assets (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead" 8/09/2015 10 2.3 "(1) Updated M2 results from the internal review * Changed "Assessment team do not say No" to "All economic assets are by definition water dependent" * Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible" * Changed "Rivertyles" to "RiverStyles"" 22/09/2015 11 2.4 "(1) Updated M2 test results for 86 assets from the external review (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review (3) Created Draft Water Dependent Asset Register file using the template V5" 20/11/2015 Dataset Citation Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 20 November 2015. Bioregional Assessment Derived Dataset. Viewed 07 June 2018, http://data.bioregionalassessments.gov.au/dataset/0bbcd7f6-2d09-418c-9549-8cbd9520ce18. Dataset Ancestors Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Travelling Stock Route Conservation Values Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From NSW Wetlands Derived From Climate Change Corridors Coastal North East NSW Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Asset list for Hunter - CURRENT Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From Commonwealth Heritage List Spatial Database (CHL) Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514

Data Coordinator Step-by-Step Guide includes:Step 1. Data spreadsheet Step 2. Complete a Dataset Inventory for each new dataset Step 3. Evaluate and Prioritize data for publication Step 4. Review security and privacy criteria Step 5. Prepare Metadata Step 6. Prepare Data Dictionary Step 7. Data Upload Step 8. Service Ticket update

The dataset is a synthetic cohort for use for the VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge. The dataset was generated using Synthea, a tool created by MITRE to generate synthetic electronic health records (EHRs) from curated care maps and publicly available statistics. This dataset represents 147,451 patients developed using the COVID-19 module. The dataset format conforms to the CSV file outputs. Below are links to all relevant information. PrecisionFDA Challenge: https://precision.fda.gov/challenges/11 Synthea hompage: https://synthetichealth.github.io/synthea/ Synethea GitHub repository: https://github.com/synthetichealth/synthea Synthea COVID-19 Module publication: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531559/ CSV File Format Data Dictionary: https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary

This report includes data from Enterprise Zone Business Projects - with exemptions on qualified property. This is Part D of a four (4) part report. A data dictionary and additional notes document are attached as resources; column header numbers can be located in the notes document for additional information. For more information, visit Business Oregon https://www.oregon.gov/biz/programs/enterprisezones

The GlobalPhone pronunciation dictionaries, created within the frame¬work of the multilingual speech and language corpus GlobalPhone, were developed in collaboration with the Karlsruhe Institute of Technology (KIT).

The GlobalPhone pronunciation dictionaries contain the pronunciations of all word forms found in the transcription data of the GlobalPhone speech & text database. The pronunciation dictionaries are currently available in 18 languages: Arabic (29230 entries/27059 words), Bulgarian (20193 entries), Croatian (23497 entries/20628 words), Czech (33049 entries/32942 words), French (36837 entries/20710 words), German (48979 entries/46035 words), Hausa (42662 entries/42079 words), Japanese (18094 entries), Polish (36484 entries), Portuguese (Brazilian) (54146 entries/54130 words), Russian (28818 entries/27667 words), Spanish (Latin American) (43264 entries/33960 words), Swedish (about 25000 entries), Turkish (31330 entries/31087 words), Vietnamese (38504 entries/29974 words), Chinese-Mandarin (73388 pronunciations), Korean (3500 syllables), and Thai (a small set with 12,420 pronunciation entries of 12,420 different words, and does not include pronunciation variants, and a larger set which contains 25,570 pronunciation entries of 22,462 different words units, and includes 3,108 entries of up to four pronunciation variants).

1) Dictionary Encoding: The pronunciation dictionary entries consist of full word forms and are either given in the original script of that language, mostly in UTF-8 encoding (Bulgarian, Croatian, Czech, French, Polish, Russian, Spanish, Thai) corresponding to the trl-files of the GlobalPhone transcriptions or in Romanized script (Arabic, German, Hausa, Japanese, Korean, Mandarin, Portuguese, Swedish, Turkish, Vietnamese) corresponding to the rmn-files of the GlobalPhone transcriptions, respectively. In the latter case the documentation mostly provides a mapping from the Romanized to the original script.

2) Dictionary Phone set: The phone sets for each language were derived individually from the literature following best practices for automatic speech processing. Each phone set is explained and described in the documentation using the international standards of the International Phonetic Alphabet (IPA). For most languages a mapping to the language independent GlobalPhone naming conventions (indicated by “M_”) is provided for the purpose of data sharing across languages to build multilingual acoustic models.

3) Dictionary Generation: Whenever the grapheme-to-phoneme relationship allowed, the dictionaries were created semi-automatically in a rule-based fashion using a set of grapheme-to-phoneme mapping rules. The number of rules highly depends on the language. After the automatic creation process, all dictionaries were manually cross-checked by native speakers, correcting potential errors of the automatic pronunciation generation process. Most of the dictionaries have been applied to large vocabulary speech recognition. In many cases the GlobalPhone dictionaries were compared to straight-forward grapheme-based speech recognition and to alternative sources, such as Wiktionary and usually demonstrated to be superior in terms of quality, coverage, and accuracy.

4) Format: The format of the dictionaries is the same across languages and is straight-forward. Each line consists of one word form and its pronunciation separated by blank. The pronunciation consists of a concatenation of phone symbols separated by blanks. Both, words and their pronunciations are given in tcl-script list format, i.e. enclosed in “{}”, since phones can carry tags, indicating the tone and length of a vowel, or the word boundary tag “WB”, indicating the boundary of a dictionary unit. The WB tag can for example be included as a standard question in the decision tree questions for capturing crossword models in context-dependent modeling. Pronunciation variants are indicated by (

5) Documentation: The pronunciation dictionaries for each language are complemented by a documentation that describes the format of the dictionary, the phone set including its mapping to the International Phonetic Alphabet (IPA), and the frequency distribution of the phones in the dictionary. Most of the pronunciation dictionaries have been successfully applied to large vocabulary speech recognition and references to publications are given when available.

This dataset contains maps of alluvial and hillslope gullies across four large blocks of lidar covering portions of the Burdekin, Fitzroy, and Normanby Catchments. The gully polygons were generated using methods developed in the NESP 5.10 project for the extraction of gullies from lidar. Lidar is detailed topographic data collected from aircraft using an airborne laser scanning system.

A significant component of the cause of declining water quality and the health of the GBR is increased land based erosion leading to sediment pollution within the rivers draining into the GBR lagoon. Gullies are a significant proportion of the erosional sediment sources within the GBR catchments. Lidar is detailed topographic data collected from aircraft using an airborne laser scanning system. Airborne lidar data and orthophotography were acquired for the three study areas (portions of the Burdekin, Fitzroy, and Normanby Catchments) in 2018 and 2019 as part of a Department of Environment and Energy program to improve investment prioritisation in the management of erosion and fine sediment losses to the reef through the establishment of a new baseline of extent of gully and streambank erosion. CSIRO undertook and oversaw the program, contracting the aerial imaging and mapping company Aerometrex to acquire, process and provide the data. The data provider supplied classified lidar point cloud data, orthophotography imagery, and Digital Elevation Models (DEM), with 0.5 m cell size, derived from point cloud data. To produce DEMs the points within the cloud must be classified into ground and non-ground points. CSIRO performed quality control analyses of the provided point cloud data and the ground/non-ground classification. The lidar acquisition achieved average point densities in the range of 20 to 30 ground points per square metre. The DEMs supplied by the data provider, via CSIRO, were used for this mapping dataset. Griffith University developed methods and processes to map alluvial and hillslope gully polygons, estimates of potential active erosion within gullies, and estimates of total volume of sediment eroded over the lifetime of these gullies. The conceptual model of the gullies that are being mapped, stems from work undertaken through NESP Project 4.9, and prior to that the MTSRF Normanby Sediment Budget Project which followed on from projects undertaken through the TRaCK Program.

Methods:

The project that produced this dataset was an investigation into developing methods to extract gullies from digital topographic data and is almost entirely a processing method. A full description of the method is available in the NESP 5.10 report. Below is a summary of the method. The data provider (Aerometrex) supplied classified lidar point cloud data, orthophotography imagery, and Digital Elevation Models (DEM), with 0.5 m raster cell size, derived from point cloud data. The DEMs were hydrologically conditioned making the modelled hydrology derived from the DEMs continuous and without disruptions. A mask of channels, roads, and dams was produced and these areas of the DEM removed from the analysis. The landscape setting was analysed and separated into rugged areas and flat to gently undulating areas, that is, hillslope and alluvial landscapes.

Landscapes, in certain configuration, exhibit a distinct break in slope that denotes a change of the predominant geomorphological processes acting on the landscape. A commonly observed break in slope in the landscape is the transition from hillslopes to alluvial floodplains. A break in slope can be observed where a mostly stable land surface changes to a predominantly eroding land surface. An example of this change is observed in large floodplains where the near horizontal surface of the floodplain is interrupted by a gully incising into the floodplain material. Another example is on hillslopes where the stable slopes are interrupted by a gully incising into the soil mantle. These breaks in slope (referred to as ‘soft margins’ of erosional landscape features) where mapped. Two different methods were used to map the soft margins in hillslope and alluvial landscapes.

The mapping of soft margins within the hillslope landscapes involved a method that performs a statistical smoothing of the elevation data within DEM to produce a smoothed land surface. The elevation of the smoothed land surface is subtracted from the original land surface. Within this subtracted layer the soft margins can be extracted.

The mapping of soft margins within the alluvial landscapes involved a combination of landscape concavity and multi-directional hillshade models. The topography stored in the DEM can be used to model the casting of shadows across the landscape when the sun is at a particular bearing and angle to the horizon. In most GIS software a model of the shadow casts by the sun is referred to as a hillshade. A procedure was followed were multiple hillshades were generated with the sun bearing and vertical angle varied so as turn the sun through 360 degrees. From these multiple hillshade layers the parts of the landscape bounded by a break in slope that denote some type of erosional landscape feature were identified.

The soft margins contain aggregations of erosional landscape features. The erosional landscape features within the soft margins are disaggregated using a surface hydrology model derive from the DEMs. This disaggregation produces a map of erosional landscape features that are individual hydrologic units.

Actively eroding gullies are a type of erosional landscape feature. The gullies are filtered from the erosional landscape features using a combination of; occurrence of bare soil (no vegetation) derive from satellite imagery; a measure of surface roughness derived from the DEM; and a measure of potential active erosion derive from the DEM. The filtering produces polygons identifying actively eroding gullies. Where the boundaries of these gully polygons overlie a distinct scarp edge in the DEM the mapped boundary is referred to as a ‘hard margin’. Estimates of total volume of sediment eroded over the lifetime of these gullies and a range of metrics (include in shapefile attribute table) were calculated for the gullies identified with the application of these mapping procedures.

Limitations of the data:

Mapping methods, processes, and filtering can generate false negatives and false positives. Within the tens of thousands of polygons there is a small possibility that a polygon is not identifying a gully, but some other closely associated geomorphic erosional landscape feature. If using this dataset to examine and evaluated specific gullies for any sort of assessment, such as, sediment control measures or rehabilitation earth works the polygons require comparison to remotely sense imagery or data and on site ground validation. Covid restrictions during the project limited the amount of ground verification that could be undertaken.

Format:

This dataset consists of three shapefiles. - BBB_Gullies_withMetrics_for_Upload_v3.shp - Fitzroy_2019_Gullies_withMetrics_for_Upload_v3.shp - Laura_2019_Gullies_withMetrics_for_Upload_v3.shp

These three shapefiles are gully polygons covering a block of the Burdekin, Fitzroy, and Normanby River catchments. The shapefile’s attribute tables contain a range of gully metrics.

Data Dictionary:

• FID: Feature ID from ArcGIS
• Id: ID used by ArcGIS for analysis
• ZONAL_ID: Unique value for extracting zonal statistics
• Area_m2: Area of the soft margin (m2)
• GullyID: Prefix for landscape class plus gully number
• GulNum: Unique number for each soft margin
• Hard_m2: Area of the hard margin (m2)
• Hard_pct: Area of hard margin as % of soft margin
• PAE_m2: Area of Potential Active Erosion margin (m2)
• PAE_pct: Area of Potential Active Erosion as % of soft margin
• VegeData: A note that data on vegetation follows
• Ht2m_plus: Area of vegetation >= 2m tall (m2)
• pct2mPlus: Area of vegetation >= 2m tall as % of soft margin
• Soft_Geom: A note that data on geometry of soft margins follows
• SoftLen_m: Length of soft margin as defined by minimum bounding rectangle (m)
• SoftWith_m: Width of soft margin as defined by minimum bounding rectangle (m)
• SftPerim_m: Perimeter of soft margin (m)
• Soft_L_W: Ratio of soft margin Length divided by Width
• S_Prm_Area: Ratio of soft margin Perimeter divided by Area
• Hard_Geom: A note that data on geometry of hard margins follows
• HardLen_m: Length of hard margin as defined by minimum bounding rectangle (m)
• HardWith_m: Width of hard margin as defined by minimum bounding rectangle (m)
• HrdPerim_m: Perimeter of hard margin (m)
• Hard_L_W: Ratio of hard margin Length divided by Width
• H_Prm_Area: Ratio of hard margin Perimeter divided by Area
• Ht_Range: A note that data on elevation range within soft margins follows
• Elev_Range: Maximum elevation minus minimum elevation within soft margin (m)
• HtRng_PAE: A note that data on elevation range within potentially active erosion follows
• PAE_Range: Maximum elevation minus minimum elevation within potentially active erosion (m)
• FlowDist: A note that data on length of maximum flow path within soft margins follows
• FlowDist_m: Maximum flow length minus minimum flow length within soft margin, using downstream operator (m)
• Slope_FL: A note that data on gully slope derived from flow length follows
• Slope_FLen: Gully Slope calc from (Ht Range div by Flow Length) div by Pi times 180
• Slope_GL: A note that data on gully slope derived from geometric length follows
• Slope_GLen: Slope using gully length from Minimum Bounding Geometry (Ht Range div by Length) div by Pi times 180
• Connected: A note that data on gully connection to channel system follows
• Conxt_Y_N: Yes or No for connected or disconnected
• Disconnect: A note that data on the distance of disconnection follows
• DisCnctDis: The distance each individual gully is disconnected from the

Programmatically generated Data Dictionary document detailing the Texas Cities service.

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    For any questions or issues related to the document, please contact the data owner of the service identified in the PDF and Credits of this portal item.


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This report includes data from Enterprise Zone Business Projects - with exemptions on qualified property. This is Part B of a four (4) part report. A data dictionary and additional notes document are attached as resources; column header numbers (1-6) can be located in the notes document for additional information. For miscellaneous local Enterprise Zone information, please visit https://data.oregon.gov/Revenue-Expense/Local-Enterprise-Zone-Reports-Miscellaneous-/bx8i-r869/about_data For more information on the Enterprise Zone program, visit https://www.oregon.gov/biz/programs/enterprisezones