The data set is a digital representation of the coarse vegetation cover in the eastern plains, eastern slopes and tablelands (generally the eastern division of NSW). The data has been visually interpreted from 1:100,000 geo-rectified Landsat TM images in 1991/92 and then converted to grid. Spatial and classification accuracy of the data is consistent and of acceptable quality. Three structural vegetation databases of the eastern portion of NSW from approximately the Western Slopes to the coast, for use as broadscale management tools. Vegetation visually interpreted from Landsat imagery. The original aim of the mapping was to produce a single layer, however different levels of detail between the datasets has precluded this. Recommended scale of use is 1:250,000. VIS_ID 181 ANZLIG ANZNS0208000011 VISID 181 Data and Resources

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

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 processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This Central West (CEN) dataset contains v2.2 of the Asset database (CEN_asset_database_20160216.mdb), a Geodatabase version for GIS mapping purposes (CEN_asset_database_GISOnly_20160216.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NIC-CEN-130-WaterDependentAssetRegister-AssetList-v20160216.xlsx), a data dictionary (CEN_asset_database_doc_20160216.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, a folder (NRM_DOC) and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below.

The Asset database supersedes the previous version of Asset database for the Central West subregion on 21 August 2015 (GUID: 5e90d2ee-a551-48c5-ba48-83e3a907fcf9). Updates to this database compared to the previous database include:

V2.2

(1) Total number of registered water assets was increased by 6 due to:

(a) The 2 assets changed M2 test to "Yes" from the review done by Ecologist group.

(b) 4 indigenous water assets from OWS were added.

V2.1

(2) Change "PAE_Region" to "Central West BA subregion boundary" for 149 NSW TS assets in field PAE_Region of table assetlist

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 separated file geodatabase joined by AID/Element ID/BARID. 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 Central Westare 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.Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " CEN_asset_database_doc_20160216.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 " CEN_asset_database_doc_20160216.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

The public version of this asset database can be accessed via the following dataset: Asset database for the Central West subregion on 16 February 2016 Public (https://data.gov.au/dataset/546107ad-27b0-4432-b17e-8876e7c9769d)

Dataset History

VersionID Date Notes

1.0 29/04/2015 Initial database

2.0 21/08/2015 v2 - additions as follows:

(1) At the request of NSW OEH, data identifying species and ecological communities listed under NSW legislation has been included in two additional attribute tables "NSW_TS" and "NSW_TEC" (a total of 149 new elements and assets, AIDs 70001-70149). However, given the extremely course catchment-scale resolution of the data as compared to the relatively fine-scale of the PAE, it is essentially a non-spatial list of species and communities which may or may not occur within the PAE. As the data was unable to be meaningfully used as a spatial dataset it was subsequently "turned off" at decision M0 (not fit for purpose), with the decision reflected in the "Assetlist"and "AssetDecisions" tables.

(2) The database has been updated to include M2 (water dependency) test results from the CEN project team, and a draft "Water-dependent asset register and asset list" spreadsheet (BA-NIC-CEN-130-WaterDependentAssetRegister-AssetList-V20150814.xlsx) has been created and is included in this dataset.

(3) Four queries have been added to the non-spatial database (mdb) (Find_All_Used_Assets, Find_All_WD_Assets, Find_Amount_Asset_in_Class and Find_Amount_Elements_in_Class) to assist project teams to identify and calculate figures to be published.

2.1 29/09/2015 "Change ""PAE_Region"" to ""Central West BA subregion boundary"" for 149 NSW TS assets in field

                           PAE_Region of table assetlist"

2.2 16/02/2016 "Total number of registered water assets was increased by 6 due to:

                             (a) The 2 assets changed M2 test to "Yes" from the review done by Ecologist group. 

                             (b) 4 indigenous water assets from OWS were added."

Dataset Citation

Bioregional Assessment Programme (2013) Asset database for the Central West subregion on 16 February 2016. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/8ac1d434-7697-4a8f-9908-814e8daf4604.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

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

• Derived From Asset database for the Central West subregion on 29 April 2015

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

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

• Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129

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

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

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

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Asset database for the Central West subregion on 21 August 2015

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

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

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014)

• Derived From Australia - Species of National Environmental Significance Database

• Derived From [NSW Office of Water Groundwater Licence Extract NIC- Oct

Volcanic geospatial database in shp and kmz formats. The dataset includes: 1) Volcanic deposits, 2) Volcanic vents, 3) Radiometric ages, and 4) Tectonic structures. This database was compiled as part of Daniel Bertin's PhD Thesis "Volcano-tectonic history and volcanic hazard assessment of the 22.5-29°S segment of the Central Volcanic Zone of the Andes". An online rendering of the database is uploaded here.

Dryad BioLINCC Survey Data 16-09-01This is the deidentified data from the 2015 cross-sectional survey of investigators who requested and received access to clinical research data from BioLINCC between 2007 and 2014.READ ME Dryad BioLINCC Survey 16-09-01.txtData Dictionary BioLINCC Survey 16-09-01This file lists and describes the variables from the 2015 cross-sectional BioLINCC survey.

These data encompass the geologic framework model for the Central Valley Hydrologic Model Version 2 (CVHM2) study. This includes (1) the Well Log Database which contains borehole information and lithology used in creating the geologic framework, (2) Well Logs with Classification Information which explains how percent coarse values were determined for each borehole, and (3) the Three-Dimensional Framework Model.

Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

Public Sector: Last 12 Months Accumulated: Central Bank of Brazil: Uses data was reported at -70,033.238 BRL mn in Apr 2019. This records an increase from the previous number of -72,668.211 BRL mn for Mar 2019. Public Sector: Last 12 Months Accumulated: Central Bank of Brazil: Uses data is updated monthly, averaging -13,248.494 BRL mn from Nov 2002 (Median) to Apr 2019, with 198 observations. The data reached an all-time high of 12,239.256 BRL mn in Jul 2003 and a record low of -80,723.484 BRL mn in Sep 2018. Public Sector: Last 12 Months Accumulated: Central Bank of Brazil: Uses data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Government and Public Finance – Table BR.FA018: Public Sector: Uses and Sources: Central Bank of Brazil: Last 12 Months Accumulated. Banco Central do Brasil (Bacen)

This geodatabase contains all the geologic map information for the Geologic Map of the San Juan caldera cluster, southwestern Colorado and is part of U.S. Geological Survey Geologic Investigations Map Series I-2799. The San Juan Mountains are the largest erosional remnant of a composite volcanic field that covered much of the southern Rocky Mountains in middle Tertiary time. The San Juan field consists mainly of intermediate-composition lavas and breccias, erupted about 35-30 Ma from scattered central volcanoes (Conejos Formation) and overlain by voluminous ash-flow sheets erupted from caldera sources. In the central San Juan Mountains, eruption of at least 8,800 km3 of dacitic-rhyolitic magma as nine major ash flow sheets (individually 150-5,000 km3) was accompanied by recurrent caldera subsidence between 28.3 Ma and about 26.5 Ma. Voluminous andesitic-dacitic lavas and breccias were erupted from central volcanoes prior to the ash-flow eruptions, and similar lava eruptions continued within and adjacent to the calderas during the period of more silicic explosive volcanism. Exposed calderas vary in size from 10 to 75 km in maximum dimension, the largest calderas being associated with the most voluminous eruptions.

This dataset records each intake of an animal in the Austin Animal Center's database, starting from May 5, 2025 (the launch date of our ShelterBuddy system). Data is refreshed hourly.

To the contents of the Central Bank's data openness action plan revised in December 2019, it is an open ODT file.

Central Bank: Assets: Gold data was reported at 105,233,059.000 TRY th in Jun 2018. This records a decrease from the previous number of 109,901,484.000 TRY th for May 2018. Central Bank: Assets: Gold data is updated monthly, averaging 51,413,761.500 TRY th from Jan 2013 (Median) to Jun 2018, with 66 observations. The data reached an all-time high of 109,901,484.000 TRY th in May 2018 and a record low of 32,761,231.000 TRY th in Jun 2013. Central Bank: Assets: Gold data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB053: Balance Sheet: Central Bank.

The East and Central European Political Elites Database (ECEPED) is a biographical database of contemporary (post-1989) national and sub-national political elites within Eastern and Central Europe. It is organized at the individual level and provides descriptive variables such as: birth, death, profession, gender, incumbency status, and others. These data were collected from electoral reports and biographical codexes generally provided through state statistical offices. It is planned to include datasets covering: members of parliament, members of regional parliaments, and cabinet ministers. IF YOU HAVE INFORMATION ON MISSING DATA HIGHLIGHTED IN RED, PLEASE CONTACT ME AT: austin.matthews@du.edu Please cite the following if you utilize any German national or German sub-national data for publication: Kerevel, Yann P., Austin S. Matthews, and Katsunori Seki. 2018. "Mixed-Member Electoral Systems, Best Loser Rules, and the Descriptive Representation of Women." Electoral Studies 57 (1): 153-162.

The Brazil Data Center Market Report is Segmented by Data Center Size (Large, Massive, Medium, and More), Tier Type (Tier 1 and 2, Tier 3, and Tier 4), Data Center Type (Hyperscale/Self-built, Enterprise/Edge, and Colocation), End User (BFSI, IT and ITES, E-Commerce, Government, Media and Entertainment, and More), and Hotspot (São Paulo, Rio De Janeiro, and More). The Market Forecasts are Provided in Terms of IT Load Capacity (MW).

Central & Eastern Europe data centers portfolio covers 283 existing data centers and 29 upcoming data centers across 4 countries.

Turkey Central Bank: LB: DE: FX: BS data was reported at 381,093,331.000 TRY th in 01 May 2020. This records a decrease from the previous number of 401,143,737.000 TRY th for 24 Apr 2020. Turkey Central Bank: LB: DE: FX: BS data is updated weekly, averaging 327,980,732.000 TRY th from Dec 2016 (Median) to 01 May 2020, with 176 observations. The data reached an all-time high of 411,706,191.000 TRY th in 13 Mar 2020 and a record low of 241,110,310.000 TRY th in 30 Dec 2016. Turkey Central Bank: LB: DE: FX: BS data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB055: Balance Sheet: Central Bank.

The Shanghai Data Center Market is Segmented by DC Size (Small, Medium, Large, Massive, Mega), Tier Type (Tier 1&2, Tier 3, Tier 4), Absorption (Utilized (Colocation Type (Retail, Wholesale, Hyperscale), End User ( Cloud & IT, Telecom, Media & Entertainment, Government, BFSI, Manufacturing, E-Commerce)) and Non-Utilized). The Market Sizes and Forecasts are Provided in Terms of Value (MW) for all the Above Segments.

Turkey Central Bank: LB: Others: TRY: Expense Accruals data was reported at 10,886.000 TRY th in Jun 2018. This records a decrease from the previous number of 61,626.000 TRY th for May 2018. Turkey Central Bank: LB: Others: TRY: Expense Accruals data is updated monthly, averaging 1,261.500 TRY th from Jan 2013 (Median) to Jun 2018, with 66 observations. The data reached an all-time high of 645,418.000 TRY th in Mar 2018 and a record low of 143.000 TRY th in Nov 2014. Turkey Central Bank: LB: Others: TRY: Expense Accruals data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB053: Balance Sheet: Central Bank.

This data set provides temperature and precipitation data from 298 meteorological stations in the Northern Tien Shan and Pamir Mountain Ranges of Central Asia, specifically from stations in Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. The period of record covered by each station is variable, however, most stations have almost 100 years of observations with the earliest record from 1879 and the latest from 2003. The data are stored as tab-delimited ASCII text format, Microsoft Excel, and PDF, and are availabe via FTP.

The size of the Austria Data Center market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 15.20% during the forecast period.A data center is an area where computer systems, networking equipment, and software are put in place to store, process, and transmit data. It provides all the necessary infrastructure for IT operations needed in organizations such as hosting websites, email servers, and database servers. Data centers serve businesses in any form: large enterprises, small startups, for they ensure digital transformation and other critical business applications.Austria is growing very high in the data center market mainly due to increased digitalization, a high need for cloud services, and demand of strong IT infrastructure. With the business adoption of cloud computing, there will also be an increase in demands for data center capacity. The demand for building a data center in this region is being influenced by regulatory requirements such as data privacy law and other needs.Austria has appeared to many data centre providers as being rather attractive due to its position at the heart of Central Europe with an excellent development of digital infrastructure complemented by a solid political climate. Austria possesses a safe power supply, high speed internet, and a quality workforce. With Austria promising to take data privacy and security on par with the world's focus on data protection, it presents an appealing market for Austrian domestic as well as international players, which in turn brings in competition and an increase in innovation. Recent developments include: October 2021: To expand the size of its data center in Vienna, Austria, NTT has started construction on the project. The Vienna 1 facility expanded by about 3,000 square meters (32,300 sq ft), making it 8,600 sqm. The facility's capacity increased by 15.2MW, and the expansion was finished by the summer of 2022.May 2021: Schwarz Group planning to establish StackIT with outside customers from two facilities in Germany and Austria. Also stated that the expansion of the second phase of DC10 with 4,500 sqm (48,400 sq ft) of IT space.January 2017: In 2017, the second NESSUS data center opened. The self-financed data center, which took two years to build, currently has room for 400 server cabinets run entirely on green electricity.. Key drivers for this market are: Increasing Need for Securing Confidential Data and Protection Against Data Loss, Growing Demand for Improving Archived Content across Channels; Ongoing efforts to promote Digitization at Workplaces. Potential restraints include: Transition from Legacy Systems Chips, Customization Challenges Leading to Implementation Issues. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.