Management Information data store for reporting on electronic service usages.

Official Union Time Tracking System captures the reporting and accounting of the representational activity for all American Federation of Government Employees (AFGE) representatives Agency wide.

In France in 2019, the source has conducted a study looking at the acceptance of personal data storage for the use of online administrative services. In accordance with the general distrust of the French towards their government and service digitalization, a disproportionate percentage shows a strong disapproval of centralized data collection, even if it would be done to simplify the user experience when using online services of their administration.

Abstract

[x[This dataset was superseded by GIP AssetList Database v1.3 20150212

GUID: e0a8bc96-e97b-44d4-858e-abbb06ddd87f

on 12/2/2015]x]

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.

This dataset contains the spatial and non-spatial (attribute) components of the Gippsland bioregion Asset List as two .mdb files, which are readable as an MS Access database or as an ESRI Personal Geodatabase.

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. All reports received associated with the WAIT process for Gippsland are 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 bioregion'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 Gippsland bioregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed.

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 "AssetList_database_GIP_v1p2_20150130.doc", located in the zip file as part of this dataset.

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 "AssetList_database_GIP_v1p2_20150130.doc" located in the zip file.

Some of the source data used in the compilation of this dataset is restricted.

Purpose

[x[*****THIS IS NOT THE CURRENT ASSET LIST*****

This dataset was superseded by GIP AssetList Database v1.3 20150212

GUID: e0a8bc96-e97b-44d4-858e-abbb06ddd87f

on 12/2/2015

THIS DATASET IS NOT TO BE PUBLISHED IN ITS CURRENT FORM]x]

Dataset History

This dataset is an update of the previous version of the Gippsland asset list database: "Gippsland Asset List V1 20141210"; ID: 112883f7-1440-4912-8fc3-1daf63e802cb, which was updated with the inclusion of a number of additional datasets from the Victorian Department of the Environment and Primary Industries as identified in the "linkages" section and below.

Victorian Farm Dam Boundaries

https://data.bioregionalassessments.gov.au/datastore/dataset/311a47f9-206d-4601-aa7d-6739cfc06d61

Flood Extent 100 year extent West Gippsland Catchment Management Authority GIP v140701

https://data.bioregionalassessments.gov.au/dataset/2ff06a4f-fdd5-4a34-b29a-a49416e94f15

Irrigation District Department of Environment and Primary Industries GIP

https://data.bioregionalassessments.gov.au/datastore/dataset/880d9042-abe7-4669-be3a-e0fbe096b66a

Landscape priority areas (West)

West Gippsland Regional Catchment Strategy Landscape Priorities WGCMA GIP 201205

https://data.bioregionalassessments.gov.au/datastore/dataset/6c8c0a81-ba76-4a8a-b11a-1c943e744f00

Plantation Forests Public Land Management(PLM25) DEPI GIP 201410

https://data.bioregionalassessments.gov.au/datastore/dataset/495d0e4e-e8cd-4051-9623-98c03a4ecded

and additional data identifying "Vulnerable" species from the datasets:

Victorian Biodiversity Atlas flora - 1 minute grid summary

https://data.bioregionalassessments.gov.au/datastore/dataset/d40ac83b-f260-4c0b-841d-b639534a7b63

Victorian Biodiversity Atlas fauna - 1 minute grid summary

https://data.bioregionalassessments.gov.au/datastore/dataset/516f9eb1-ea59-46f7-84b1-90a113d6633d

A number of restricted datasets were used to compile this database. These are listed in the accompanying documentation and below:

• The Collaborative Australian Protected Areas Database (CAPAD) 2010

• Environmental Assets Database (Commonwealth Environmental Water Holder)

• Key Environmental Assets of the Murray-Darling Basin

• Communities of National Environmental Significance Database

• Species of National Environmental Significance

• Ramsar Wetlands of Australia 2011

Dataset Citation

Bioregional Assessment Programme (2015) GIP AssetList Database v1.2 20150130. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/6f34129d-50a3-48f7-996c-7a6c9fa8a76a.

Dataset Ancestors

• Derived From Flood Extent 100 year extent West Gippsland Catchment Management Authority GIP v140701

• Derived From Surface Water Economic Entitlements GIP 20141219

• Derived From West Gippsland Regional Catchment Strategy Landscape Priorities WGCMA GIP 20121205

• Derived From Irrigation District Department of Environment and Primary Industries GIP

• Derived From Surface Water and Groundwater Entitlement Data with Volumes - DEPI Regs Cat6 Victoria 20141218

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Victorian Water Supply Protection Areas

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Southern Rural Water SW Locations with BOM Regulations Category 6 Volumes Gippsland 20150430

• Derived From Gippsland Project boundary

• Derived From Victorian Groundwater Management Areas

• Derived From Plantation Forests Public Land Management(PLM25) DEPI GIP 201410

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Surface Water Entitlement Locations Gippsland Southern Rural Water 20141218

• Derived From Ramsar Wetlands of Australia

• Derived From National Groundwater Information System Victorian Extract (2014-03-21)

• Derived From GEODATA TOPO 250K Series 3

• Derived From Groundwater Licences Entitlement Volume To Bores Vic DEPI 20141021

• Derived From Groundwater Economic Elements Gippsland 20141120

• Derived From Commonwealth Heritage List Spatial Database (CHL)

• Derived From Potential Groundwater Dependent Ecosystems for West Gippsland Catchment Management Authority

• Derived From Victorian Biodiversity Atlas flora - 1 minute grid summary

• Derived From Unreg surface water licences DELWP Gippsland 20150301

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Gippsland Basin bioregion Asset List v01 - 20141210

• Derived From Victorian Farm Dam Boundaries

• Derived From Gippsland Basin bioregion Preliminary Assessment Extent (PAE)

• Derived From [Victoria Regional CMA - Water Asset Information Tool - WAIT

The United States Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage [NAS], Storage Area Network [SAN], Direct Attached Storage [DAS]), Storage Type (Traditional Storage, All-Flash Storage, Hybrid Storage), End User (IT and Telecommunication, BFSI, Government, Media and Entertainment, and Other End Users). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Database used to store client data both Identity and customer relationship management.

This dataset contains FEMA applicant-level data for the Individuals and Households Program (IHP). All PII information has been removed. The location is represented by county, city, and zip code. This dataset contains Individual Assistance (IA) applications from DR1439 (declared in 2002) to those declared over 30 days ago. The full data set is refreshed on an annual basis and refreshed weekly to update disasters declared in the last 18 months. This dataset includes all major disasters and includes only valid registrants (applied in a declared county, within the registration period, having damage due to the incident and damage within the incident period). Information about individual data elements and descriptions are listed in the metadata information within the dataset.rnValid registrants may be eligible for IA assistance, which is intended to meet basic needs and supplement disaster recovery efforts. IA assistance is not intended to return disaster-damaged property to its pre-disaster condition. Disaster damage to secondary or vacation homes does not qualify for IHP assistance.rnData comes from FEMA's National Emergency Management Information System (NEMIS) with raw, unedited, self-reported content and subject to a small percentage of human error.rnAny financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting. rnCitation: The Agency’s preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnDue to the size of this file, tools other than a spreadsheet may be required to analyze, visualize, and manipulate the data. MS Excel will not be able to process files this large without data loss. It is recommended that a database (e.g., MS Access, MySQL, PostgreSQL, etc.) be used to store and manipulate data. Other programming tools such as R, Apache Spark, and Python can also be used to analyze and visualize data. Further, basic Linux/Unix tools can be used to manipulate, search, and modify large files.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.rnThis dataset is scheduled to be superceded by Valid Registrations Version 2 by early CY 2024.

The Asia-Pacific Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage (NAS), Storage Area Network (SAN), and Direct Attached Storage (DAS)), Storage Type (Traditional Storage, All-Flash Storage, and Hybrid Storage), End User (IT & Telecommunication, BFSI, Government, Media & Entertainment, and Other End Users), and Country. The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Mexico Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage [NAS], Storage Area Network [SAN], Direct Attached Storage [DAS]), Storage Type (Traditional Storage, All-Flash Storage, Hybrid Storage), End User (IT and Telecommunication, BFSI, Government, Media and Entertainment, and Other End Users). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

United States Ohio: Liquor Store Revenue data was reported at 1,079,367.000 USD th in 2015. This records an increase from the previous number of 1,008,258.000 USD th for 2014. United States Ohio: Liquor Store Revenue data is updated yearly, averaging 336,800.000 USD th from Jun 1957 (Median) to 2015, with 57 observations. The data reached an all-time high of 1,079,367.000 USD th in 2015 and a record low of 202,100.000 USD th in 1958. United States Ohio: Liquor Store Revenue data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.F044: Revenue & Expenditure: State and Local Government: Ohio.

Abstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from the Local Government Areas of Australia dataset. You can find a link to the parent dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. LGA extracted for the Hunter subregion The digital boundaries for this edition of the ASGC are consistent with the spatial units described in the structures of the ASGC …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from the Local Government Areas of Australia dataset. You can find a link to the parent dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. LGA extracted for the Hunter subregion The digital boundaries for this edition of the ASGC are consistent with the spatial units described in the structures of the ASGC 2011. Date of effect of this edition is 1 July 2011. Digital boundaries are for Statistical Local Area (SLA), Statistical Subdivision (SSD), Statistical Division (SD), Local Government Area (LGA), Statistical District (SDIST), Major Statistical Region (MSR), Statistical Region (SR), Statistical Region Sector (SRS) and State/Territory (STE). This is the final edition of the ASGC. To assist in the transition to the new Statistical Geography, the Australian Statistical Geography Standard (ASGS), the 2011 SLAs have been aggregated up from the 2011 Mesh Blocks (MB). The 2011 MBs are also the building blocks for the 2011 ASGS. Purpose Refer Product Brief: https://data.bioregionalassessments.gov.au/datastore/dataset/1d139f3a-fc76-4717-863c-3689413ee9c0 ASGC Digital Boundaries Australia 2011 Product Brief.pdf Dataset History LGA extracted for the Hunter subregion MB boundaries are aggregated up to form the 2011 SLA boundaries. The MB boundaries were created using various sources including the PSMA digital topographic datasets and ABS SLA boundaries, zoning information from state planning agencies and imagery. Higher level spatial units are aggregated from the SLA level. Dataset Citation Bioregional Assessment Programme (2011) Hunter Local Government Areas. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/45cf9e06-6114-4d68-b5c9-ae0b0ee9d8db. Dataset Ancestors Derived From Hunter subregion boundary Derived From Natural Resource Management (NRM) Regions 2010 Derived From Bioregional Assessment areas v03 Derived From Local Government Areas of Australia Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

The Taiwan Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage (NAS), Storage Area Network (SAN), Direct Attached Storage (DAS), and Other Technologies), by Storage Type (Traditional Storage, All-Flash Storage, and Hybrid Storage), by End-User (IT & Telecommunication, BFSI, Government, Media & Entertainment and Other End-Users). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

United States Mississippi: Liquor Store Expenditure data was reported at 279,212.000 USD th in 2016. This records an increase from the previous number of 256,248.000 USD th for 2015. United States Mississippi: Liquor Store Expenditure data is updated yearly, averaging 91,573.500 USD th from Jun 1957 (Median) to 2016, with 58 observations. The data reached an all-time high of 279,212.000 USD th in 2016 and a record low of 0.000 USD th in 1966. United States Mississippi: Liquor Store Expenditure data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.F033: Revenue & Expenditure: State and Local Government: Mississippi.

The Brazil Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage (NAS), Storage Area Network (SAN), and Direct Attached Storage (DAS)), Storage Type (Traditional Storage, All-Flash Storage, Hybrid Storage), and End User (IT & Telecommunication, BFSI, Government, Media & Entertainment, and Other End-User). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Office of the Chief Technology Officer (OCTO), within the District of Columbia (DC) government, manages the District’s data program. This includes open data, data curation, data integration, data storage, data science, data application development and Geographic Information Systems (GIS). The open data handbook explains the process and steps OCTO undertakes when an agency submits an open dataset for publication. The handbook outlines dataset rules, documentation requirements, and policies to make data consistent and standardized. This applies to any dataset submitted for publication on the Open Data DC portal that is classified as Level 0: Open as defined in the District’s Data Policy. For previous versions of the handbook visit https://opendata.dc.gov/pages/handbook.

Search API for looking up addresses and roads within the catchment. The api can search for both address and road, or either. This dataset is updated weekly from VicMap Roads and Addresses, sourced …Show full descriptionSearch API for looking up addresses and roads within the catchment. The api can search for both address and road, or either. This dataset is updated weekly from VicMap Roads and Addresses, sourced via www.data.vic.gov.au. Use The Search API uses a data.gov.au datastore and allows a user to take full advantage of full test search functionality. An sql attribute is passed to the URL to define the query against the API. Please note that the attribute must be URL encoded. The sql statement takes for form as below: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 ')) LIMIT 10 The above will select the top 10 results from the API matching the input 'term', and return the display name as well as an x and y coordinate. The full URL for the above query would be: https://data.gov.au/api/3/action/datastore_search_sql?sql=SELECT display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 ')) LIMIT 10) Fields Any field in the source dataset can be returned via the API. Display, x and y are used in the example above, but any other field can be returned by altering the select component of the sql statement. See examples below. Filters Search data sources and LGA can also be used to filter results. When not using a filter, the API defaults to using all records. See examples below. Source Dataset A filter can be applied to select for a particular source dataset using the 'src' field. The currently available datasets are as follows: 1 for Roads 2 for Address 3 for Localities 4 for Parcels (CREF and SPI) 5 for Localities (Propnum) Local Government Area Filters can be applied to select for a specific local government area using the 'lga_code' field. LGA codes are derrived from Vicmap LGA datasets. Wimmeras LGAs include: 332 Horsham Rural City Council 330 Hindmarsh Shire Council 357 Northern Grampians Shire Council 371 West Wimmera Shire Council 378 Yarriambiack Shire Council Examples Search for the top 10 addresses and roads with the word 'darlot' in their names: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('darlot', ' ', ' & ')) LIMIT 10) example Search for all roads with the word 'perkins' in their names: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('perkins', ' ', ' %26 ')) AND src=1 example Search for all addresses with the word 'kalimna' in their names, within Horsham Rural City Council: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('kalimna', ' ', ' %26 ')) AND src=2 and lga_code=332 example Search for the top 10 addresses and roads with the word 'green' in their names, returning just their display name, locality, x and y: SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('green', ' ', ' %26 ')) LIMIT 10 example Search all addresses in Hindmarsh Shire: SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE lga_code=330 example

The Next-Generation Data Storage Market is projected to grow at 13.3% CAGR, reaching $157.34 Billion by 2029. Where is the industry heading next? Get the sample report now!

The size and share of the market is categorized based on Application (BFSI, Defence and Aerospace, Education, Government, Healthcare, Telecom & IT) and Product (Consumer Storage, Enterprise Storage) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The global secure data destruction market size was valued around USD 6.50 billion in 2022 and is expected to grow around USD 12.50 billion by 2030 with a compound annual growth rate (CAGR) of approximately 9.79% between 2023 and 2030.

The Europe Data Center Storage Market Report is Segmented by Storage Technology (Network Attached Storage (NAS), Storage Area Network (SAN), and Direct Attached Storage (DAS)), Storage Type (Traditional Storage, All-Flash Storage, and Hybrid Storage), End User (IT & Telecommunication, BFSI, Government, Media & Entertainment, and Other End Users), and Country. The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.