1. Species’ ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species’ currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an integrated SDM database (iSDMdb).

2. The iSDMdb is a spatial database of predicted sites in a species’ prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility, we used the endangered New Zealand sea lion (Phocarctos hookeri). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management.

3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the iSDMdb’s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species’ range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.

4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The iSDMdb addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an iSDMdb using open-source tools, and how to make diverse, complex assessments more accessible for end-users.

Methods We created an iSDMdb using the New Zealand sea lion (Phocarctos hookeri) as an example. Enclosed are the R scripts and table summaries that describe the methods.

The original data used for this study were obtained from Land Information New Zealand (http://data.linz.govt.nz), Stats New Zealand (https://data.mfe.govt.nz), New Zealand Department of Conservation (DOC), https://koordinates.com, Ministry for the Environment, and Landcare New Zealand (https://lris.scinfo.org.nz), under New Zealand Creative Commons Attribution 3.0 and Landcare Data Use licensing. New Zealand sea lion location data were obtained from DOC via Dragonfly Data Science (https://sealions.dragonfly.co.nz/demographics/) and Frans et al. 2018 (https://doi.org/10.5061/dryad.14mt7).

See the main text of the corresponding Methods in Ecology and Evolution publication for more details.

This dataset contains links to data supporting the journal article, "High radiative forcing climate scenario relevance analyzed with a ten-million-member ensemble". Note that all the data and code is also linked to from the journal article itself. Citation information for this dataset can be found in Data.gov's References section.

This is the supporting data for the manuscript: Ego-relevance in Team Production. It includes: - Code and data to reproduce regression analysis (in Stata). - Code and data to reproduce figures (in R).

During a survey carried out among decision-makers in charge of customer engagement/retention strategy from 20 countries worldwide, ** percent of respondents stated that they thought it was important or critical to collect customer channel engagement data; ************* named real-time experience in this context.

This archive contains the accompanying data and source code of our study 'Relations Between Relevance Assessments, Bibliometrics and Altmetrics' submitted to BIR 2020 @ ECIR 2020. In order to reproduce our study, the iSearch collection is needed. After downloading this archive, place the folders `PF/` and `PN/` in the root directory. The directory `spreadsheets` contains the results of our final evaluation. The required data for these results can be found in the directory `data`, that contains the output of the scripts (in the directory `src`).

Abstract:

Relevance assessment in retrieval test collections and citations/mentions of scientific documents are two different forms of relevance decisions. To investigate the relations between these direct and indirect forms of relevance decisions, we combine arXiv data with Web of Science and Altmetrics data. In this new collection, we assess the effect of relevance ratings on measured perception in the form of citations or mentions, likes, tweets, et cetera. The impact of our work is that we could show a relation between direct relevance assessments and indirect relevance signals.

Between 2015 and 2022, numerous consuming countries in North America, Europe, and Oceania have proposed or passed legislation aimed to improve the environmental and social sustainability of businesses’ supply chains. These fall into three categories: disclosure-based legislation, mandating that companies report on sustainability-related risks and their approach to reducing it; due diligence legislation, which mandates companies to implement procedures to assess, mitigate, and remediate sustainability-related risks in their supply chains; and trade-based legislation, which prohibits the import of specific types of goods linked to adverse outcomes. We can further distinguish between single-issue legislation on the issues of labor problems (modern slavery/forced labor/child labor) and deforestation in the supply chain, and legislations with broader human rights and environmental scope. This database and the related report aim to provide an overview of the status, scope, and requirements of various laws that are tabled or already in force, with a particular focus on how they are likely to affect the coffee sector and actors within it.

2023 update:The 2023 version of the database updates the status of the respective regulations and expands the scope of search also to emerging consuming countries (e.g. in Asia) and producing countries (e.g. in Latin America). Please see country scope below. In the 2023 version of the database, we further added the category of "National Strategies, Action Plans, and Guidelines" to refer to soft law approaches that are more common in certain regions (such as Asia) to date. For completeness, we furthermore added select regulations (e.g. the US Uyghur Forced Labor Prevention Act) that have likely low relevance to the coffee sector but indicate a broader trend of the use of due diligence and trade instruments. The risk level of each legislation for coffee actors is described in column AQ. Green highlighted names of legislations highlight new additions to the database, while green highlighted cells indicate changes in criteria of legislations that were already part of the 2022 database.

This database was last updated on 14.09.2023, and contains information that was correct to the best of the authors' knowledge up to that date.

Introducing Our Comprehensive Global B2B Contact Data Solution

In today’s rapidly evolving business landscape, having access to accurate, comprehensive, and actionable information is not just an advantage—it’s a necessity. Introducing our Global B2B Contact Data Solution, meticulously crafted to empower businesses worldwide by providing them with the tools they need to connect, expand, and thrive in the global market.

What Distinguishes Our Data?

Our Global B2B Contact Data is a cut above the rest, designed with a laser focus on identifying and connecting with pivotal decision-makers. With a database of over 220 million meticulously verified contacts, our data goes beyond mere numbers. Each entry includes business emails and phone numbers that have been thoroughly vetted for accuracy, ensuring that your outreach efforts are both meaningful and effective. This data is a key asset for businesses looking to forge strong connections that are crucial for global expansion and success.

Unparalleled Data Collection Process

Our commitment to quality begins with our data collection process, which is rooted in a robust and reliable approach: - Dynamic Publication Sites: We draw data from ten dynamic publication sites, serving as rich sources for the continuous and real-time creation of our global database. - Contact Discovery Team: Complementing this is our dedicated research powerhouse, the Contact Discovery Team, which conducts extensive investigations to ensure the accuracy and relevance of each contact. This dual-sourcing strategy guarantees that our Global B2B Contact Data is not only comprehensive but also trustworthy, offering you the reliability you need to make informed business decisions.

Versatility Across Diverse Industries

Our Global B2B Contact Data is designed with versatility in mind, making it an indispensable tool across a wide range of industries: - Finance: Enable precise targeting for investment opportunities, partnerships, and market expansion. - Manufacturing: Identify key players and suppliers in the global supply chain, facilitating streamlined operations and business growth. - Technology: Connect with innovators and leaders in tech to foster collaborations, drive innovation, and explore new markets. - Healthcare: Access critical decision-makers in healthcare for strategic partnerships, market penetration, and research collaborations. - Retail: Engage with industry leaders and stakeholders to enhance your retail strategies and expand your market reach. - Energy: Pinpoint decision-makers in the energy sector to explore new ventures, investments, and sustainability initiatives. - Transportation: Identify key contacts in logistics and transportation to optimize operations and expand into new territories. - Hospitality: Connect with executives and decision-makers in hospitality to drive business growth and market expansion. - And Beyond: Our data is applicable across virtually every industry, ensuring that no matter your sector, you have the tools needed to succeed.

Seamless Integration for Holistic Insights

Our Global B2B Contact Data is not just a standalone resource—it’s a vital component of a larger data ecosystem that offers a panoramic view of the business landscape. By seamlessly integrating into our wider data collection framework, our Global B2B Contact Data enables you to: - Access Supplementary Insights: Gain additional valuable insights that complement your primary data, providing a well-rounded understanding of market trends, competitive dynamics, and global key players. - Informed Decision-Making: Whether you’re identifying new market opportunities, analyzing industry trends, or planning global expansion, our data equips you with the insights needed to make strategic, data-driven decisions.

Fostering Global Connections

In today’s interconnected world, relationships are paramount. Our Global B2B Contact Data acts as a powerful conduit for establishing and nurturing these connections on a global scale. By honing in on decision-makers, our data ensures that you can effortlessly connect with the right individuals at the most opportune moments. Whether you’re looking to forge new partnerships, secure investments, or venture into uncharted B2B territories, our data empowers you to build meaningful and lasting business relationships.

Commitment to Privacy and Security

We understand that privacy and security are of utmost importance when it comes to handling data. That’s why we uphold the highest standards of privacy and security, ensuring that all data is managed ethically and in full compliance with global privacy regulations. Businesses can confidently leverage our data, knowing that it is handled with the utmost care and respect for legal requirements.

Continuous Enhancement for Superior Data Quality

Adaptability and continuous improvement are at the core of our ethos. We are committed to consistently enhancing our B2B C...

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

cohortsCohort descriptions used to evaluate and train the L-PCRS system.judgmentsRelevance judgments used to train and evaluate the L-PCRS system.trained_modelsRandom Forest classifiers trained on each dataset, in Lemur's RankLib.jar format.acquiring_the_text_collectionsInstructions for where to find the TUH EEG corpus.

This statistic shows the summarized percentage of companies, by industry, which reported that the gathering, analysis, and utilization of data had a high level of significance on decision-making, today and in five years, according to a 2016 survey conducted by PwC. As of 2016, 55 percent of industrial manufacturing companies surveyed said that data played a highly significant role in decision-making.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Database of Communities of National Environmental Significance stores maps, taxonomic, ecological, and management information about Communities of National Environmental Significance listed in the Environment Protection and Biodiversity Conservation (EPBC) Act 1999 as threatened ecological communities.

Credit:

State and Commonwealth Herbaria, Museums and Conservation Agencies Centre for Plant Biodiversity Research Australian Government Department of the Environment, Environmental Resources Information Network

External accuracy:

The positional accuracy of spatial data is a statistical estimate of the degree to which planimetric coordinates and elevations of features agree with their real world values. The planimetric accuracy attainable in the vector data will be composed of errors from three sources:

1. The positional accuracy of the source material

2. Errors due to the conversion processes.

3. Errors due to the manipulation processes.

This specification cannot prescribe a figure for the planimetric accuracy of the existing source material used for capture of community distributions as it has already been produced. The errors due to the digitising process depend on the accuracy of the digitising table set-up or the scanner resolution, systematic errors in the equipment, errors due to software and errors specific to the operator. An accepted standard for digitising is that the line accuracy should be within half a line width.

Non Quantitative accuracy:

Tests are undertaken to ensure that there are no errors in attributes:

• The spatial resolution of the data is reflected in the Presence Categories

• Presence categories are one of:

* Community known to occur within area

* Community likely to occur within area

* Community may occur within area (general indication only)

Conceptual consistency:

Tests undertaken for logical consistency:

• Names of export files and data quality table are correct

• Table names are valid

• Item names in coverages are valid

• Item names are present in coverage attribute files

• Label points and entity point features have only one coordinate pair

• The Arc/Info coverages can be generated, have attributes attached and be 'built'

• In polygon coverages there are no label errors i.e. every polygon has one and only one polygon label point

• Data format, projection and data type are correct

• There are no overshoots, i.e. arc overhangs at intersections (1% error acceptable)

• There are no undershoots, i.e. arcs failing to meet at intersections (0.5% error acceptable)

• There are no new polygons smaller than the minimum specified area (5% error acceptable)

• There are no new linear features shorter than the minimum length (5% error acceptable)

• There are no artefacts such as spikes or deviations visible at 1:125 000 (5% error acceptable)

• Separate covers have exactly coincident lines where intended (5% error acceptable)

Completeness omission:

The database is continually being updated as the lists of threatened ecological communities on schedules of the EPBC Act are amended.

The Species of National Environmental Significance database is available at

https://www.environment.gov.au/science/erin/databases-maps/snes

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Spatial information is stored in a geographic information system and links to the Species Profile tables through the community identifier.

Source data were provided from a range of government, industry and non-government organisations.

Testing is carried out using a combination of expert opinion and on-screen checks.

Dataset Citation

Department of the Environment (2015) Communities of National Environmental Significance Database - RESTRICTED - Metadata only. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/c01c4693-0a51-4dbc-bbbd-7a07952aa5f6.

This statistic shows the importance of big data analysis and machine learning technologies worldwide as of 2019. Tensorflow was seen as the most important big data analytics and machine learning technology, with 59 percent of respondents stating that it was important to critial for their organization.

Legend for interpreting the data of the

Kiel South Asian Typological Database

This corpus was originally compiled under the direction of John Peterson by Jessica Katiuscia Ivani, with contributions by Netra Prasad Paudyal, Nikita König, Lennart Chevallier, Anika Besser, Nellia Bleyer, Sarah Anders and Josephine Hennig in the project “Towards a linguistic prehistory of eastern central South Asia (and beyond)”, financed by the German Research Council (DFG, Project Grant 326697274). The database has since been considerably expanded and the data have been re-checked and corrected, where necessary, by John Peterson and Lennart Chevallier.

This database includes information on up to 237 features (described below) for 40 languages from the Indo-Aryan, Munda and Dravidian families, as well as the isolates Kusunda and Nihali. Of these 237 features, 98 derive from the Grambank database of the Glottobank research consortium and were compiled by the members of our project in cooperation with that project. We include here only those 98 features from that database which we felt are of particular relevance for South Asia.

All updates will be documented in detail with respect to the changes made, together with the date of the respective update.

Feature values: The features are encoded as follows for all languages:

1 – the respective feature is found in this language

0 – the respective feature is not found in this language

? – it is not clear from the available data sources whether this feature is found in the respective language or not

NA – this section of the data has not yet been completed for the relevant data

The values of the multistate features – GB024, GB025, GB065, & GB193 – state, whether the adnominal element precedes (1) or follows (2) the noun or both orders occur (3).

Features labeled “GB” are features from the original Grambank database compiled by members of our project. As the labeling of features in that database may have changed somewhat since that time, the labels found here may no longer correlate one-to-one with those features. We hope to synchronize these labels in the near future, but until then users of these features will have to check these on their own.

The feature labels “NGB”, “JPP” and “SA” refer only to different stages during the compilation of the data in our own project and are not relevant to the analysis of the data themselves.

The primary areas of grammar covered by the relevant features (e.g., ergativity, classifiers, negation, number, etc.) have been indicated on the right-hand side of the features list for many of these features. This list is not exhaustive and is only intended to serve as an initial orientation.

Use of these data

These data may be freely used in scientific research under the following two conditions:

1. That you properly cite this database, including the following information:
   
   Ivani, Jessica Katiuscia, Peterson, John & Chevallier, Lennart. 2022. The Kiel South Asian Typological Database. https://doi.org/10.5281/zenodo.7298604. [Date of last access].
   
2. That you inform us in the event of incorrect data in the table, should you find any, so that we can recheck these ourselves.
   
   We would also be grateful if you would send us a copy of your work using these data.

We expressly welcome input on the data from experts in the languages contained in this database and on further languages of the subcontinent!

USAID, in partnership with the host governments and international donors, is implementing a robust set of development programs to address the second order impacts and ensure that Guinea, Sierra Leone, Liberia and other nations in the region are prepared to effectively prevent, detect, and respond to future outbreaks. This data asset includes data on the accuracy and precision of different test kit methods commonly used in the field in emergency response to test chlorine at the 0.5% and 0.05% levels, in comparison to gold standard methods.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

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 Database Monitoring Tool market is experiencing robust growth, driven by the increasing adoption of cloud-based databases, the rise of big data analytics, and the critical need for ensuring database uptime and performance in today's digital economy. The market, estimated at $15 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% through 2033. This growth is fueled by several key factors. Firstly, the expanding complexity of modern database systems necessitates sophisticated monitoring solutions to proactively identify and resolve performance bottlenecks. Secondly, the growing reliance on data-driven decision-making across industries, from finance to healthcare, mandates robust database performance to support critical business operations. Finally, stringent regulatory compliance requirements and the increasing frequency of cyberattacks emphasize the importance of real-time database monitoring for security and data integrity. The market is segmented by deployment (cloud-based and on-premises) and user type (large enterprises and SMEs). While cloud-based solutions are currently gaining significant traction due to their scalability and cost-effectiveness, on-premises solutions remain relevant for organizations with stringent security and data sovereignty requirements. Large enterprises are the dominant segment due to their larger IT infrastructure and higher budgets. However, SMEs are showing increasing adoption driven by the availability of cost-effective solutions and growing awareness of the importance of database performance. Competition in this market is fierce, with a range of established players and emerging startups vying for market share. Key vendors like Dynatrace, Datadog, and SolarWinds are leveraging their established brand reputation and extensive feature sets to maintain their leadership positions. Meanwhile, newer entrants are focusing on innovation and niche solutions, particularly in areas like AI-powered anomaly detection and automated remediation. Geographic growth is expected across all regions, with North America and Europe remaining the leading markets initially, followed by significant growth in the Asia-Pacific region driven by digital transformation initiatives. The market faces some restraints, primarily the complexities of integrating monitoring tools into diverse IT environments and the challenge of managing increasing data volumes efficiently. However, continuous technological advancements and the increasing availability of skilled professionals are mitigating these challenges, paving the way for sustained market expansion.

The primary research question for which these data have been used, was: ‘How patient-relevant are outcomes measured in clinical trials for breast cancer drugs?’. Subquestions were: 1. Which treatment outcomes are relevant for breast cancer patients? 2. Which outcome measures are used in clinical trials for breast cancer drugs? 3. How much overlap is there between patient-relevant outcomes and outcomes measured in clinical trials?The dataset has been used to answer subquestion 2. Data have been obtained by searching Clinicaltrials.gov for trials conducted between January 2014 and March 2024 inclusive. Further inclusion criteria were that studies had to be phase III trials and had to focus on breast cancer, adults (18-64 years old) and drugs. Interventions focusing on lifestyle changes, Chinese medicine, anaesthesia, surgery and diagnostic methods were excluded. Ultimately, 264 trials were included and forty-five excluded. To determine the outcome measures used, the study plan of every included trial was reviewed and recorded on the data sheet.

This workspace provides information for the AERA Open article:Blazar, D., & Kraft, M. A. (2019). Balancing Rigor, Replication, and Relevance: A Case for Multiple-Cohort, Longitudinal Experiments. AERA Open, 5(3), 2332858419876252.Only the programming file (in Stata) is included, as the data are restricted use under data use agreements.

An example of search strategy in the PubMed database is provided below.