In 2020, most enterprises according to respondents have multiple tools or applications to perform various data management functions. Of the functions, back up/recovery and data classification were the approaches used most frequently with multiple apps, according to ** percent of respondents for both.

Many challenges come with adopting a mixed-environment model. In 2023 about ** percent of respondents reported that data storage costs are one of the biggest challenges in this regard.

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

• esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.
• statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

Fieldname	Description
abstract_data	A brief synopsis or abstract about the learning resource
abstract_format	Declaration for how the abstract description will be represented.
access_conditions	Conditions upon which the resource can be accessed beyond cost, e.g., login required.
access_cost	Yes or No choice stating whether othere is a fee for access to or use of the resource.
accessibililty_features_name	Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.
accessibililty_summary	A human-readable summary of specific accessibility features or deficiencies.
author_names	List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system
author_org - name - name_identifier - name_identifier_type	- Name of organization authoring the learning resource. - The unique identifier for the organization authoring the resource. - The identifier scheme associated with the unique identifier for the organization authoring the resource.
authors - givenName - familyName - name_identifier - name_identifier_type	- Given or first name of person(s) authoring the resource. - Last or family name of person(s) authoring the resource. - The unique identifier for the person(s) authoring the resource. - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.
citation	Preferred Form of Citation.
completion_time	Intended Time to Complete
contact - name - org - email	- Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - (excluded) Contact email address.
contributor_orgs - name - name_identifier - name_identifier_type - type	- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person. - The unique identifier for the organization contributing to the resource. - The identifier scheme associated with the unique identifier for the organization contributing to the resource. - Type of contribution to the resource made by an organization.
contributors - familyName - givenName - name_identifier - name_identifier_type	- Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.
contributors.type	Type of contribution to the resource made by a person.
created	The date on which the metadata record was first saved as part of the input workflow.
creator	The name of the person creating the MD record for a resource.
credential_status	Declaration of whether a credential is offered for comopletion of the resource.
ed_frameworks - name - description - nodes.name	- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability. - A description of one or more subcategories of an educational framework to which a resource is associated. - The name of a subcategory of an educational framework to which a resource is associated.
expertise_level	The skill level targeted for the topic being taught.
id	Unique identifier for the MD record generated by the system in UUID format.
keywords	Important phrases or words used to describe the resource.
language_primary	Original language in which the learning resource being described is published or made available.
languages_secondary	Additional languages in which the resource is tranlated or made available, if any.
license	A license for use of that applies to the resource, typically indicated by URL.
locator_data	The identifier for the learning resource used as part of a citation, if available.
locator_type	Designation of citation locatorr type, e.g., DOI, ARK, Handle.
lr_outcomes	Descriptions of what knowledge, skills or abilities students should learn from the resource.
lr_type	A characteristic that describes the predominant type or kind of learning resource.
media_type	Media type of resource.
modification_date	System generated date and time when MD record is modified.
notes	MD Record Input Notes
pub_status	Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.
published	Date of first broadcast / publication.
publisher	The organization credited with publishing or broadcasting the resource.
purpose	The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.
rating	The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.
ratings	Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.
resource_modification_date	Date in which the resource has last been modified from the original published or broadcast version.
status	System generated publication status of the resource w/in the registry as a yes for published or no for not published.
subject	Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.
submitter_email	(excluded) Email address of

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

Financial overview and grant giving statistics of Data Management Association International Kansas City Chapter

The statistic shows the share of data management components that are run in the cloud worldwide, as of October 2016. According to the survey, ** percent of respondents indicated that their data warehouses were running in the cloud.

Data created during Computer Statistics assignment

Context and methodology

• This is used for the project in the context of the "Introduction to Research Data Management" course, 2024 winter semester. Originally it was made for a homework assignment in the "Computer Statistics" course, 2023 winter semester.
• The dataset consists of the following: code (and comment) written in the R markdown language that is to be compiled and executed in order to generate the 2 datasets created in the project; .pdf file generated from compiling and executing the aforementioned R code using RStudio; .txt file generated as part of one of the exercises in the assignment, also by compiling and executing the R code.
• The code was written by Vseslav Levchenko in R, using RStudio.

Technical details

• The code was written in RStudio and it is recommended to use it when working with R, however it is not strictly necessary. However, it is required to install the R language itself. For the other files, standard software like Microsoft Excel and any PDF reader are all that is needed.
• The code also contains necessary comments, and a .pdf file with the assignment's tasks is provided separately.

Financial overview and grant giving statistics of Data Management Association Puget Sound

The Enterprise Data Management (EDM) Tools market plays a pivotal role in modern businesses, where data is a critical asset. These tools enable organizations to effectively manage, govern, and leverage their data assets across various departments and systems. This comprehensive approach to data management not only e

The global dynamic data management system market is likely to be valued at US$ 33,960.7 million in 2023. From 2023 to 2033, the market for dynamic data management system is likely to expand at a CAGR of 10.6% to reach US$ 1,24,745.1 million by 2033.

Data Points	Key Statistics
Global Dynamic Data Management System Market CAGR (2023 to 2033)	10.6%
Anticipated Market Value (2023)	US$ 33,960.7 million
Global Dynamic Data Management System Market (2033)	US$ 1,24,754.1 million

Report Scope

Report Attribute	Details
Market Value in 2023	US$ 33,960.7 million
Market Value in 2033	US$ 1,24,745.1 million
Growth Rate	CAGR of 10.6% from 2023 to 2033
Base Year for Estimation	2022
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Quantitative Units	Revenue in US$ million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis
Segments Covered	Deployment Type Verticals Region
Regions Covered	North America Europe Asia Pacific Middle East and Africa Latin America
Key Countries Profiled	United States Canada Brazil Mexico Germany United Kingdom France Spain Italy China Japan South Korea Malaysia Singapore Australia GCC South Africa Israel
Key Companies Profiled	Oracle Corporation Microsoft Corporation IBM Corporation SAP SE Teradata EMBARCADERO Inc. Couchbase Inc. BMC Software Inc. Actian Corporation SolarWinds MongoDB ManageEngine Altibase
Report Customization & Pricing	Available Upon Request

During a survey on customer experience (CX) among businesses conducted in the United States in 2019, participants were asked how their organizations cope with increasing data volume originating from digital channels. One in *** respondents answered that their organization has a dedicated business analytics team in charge of interpreting data. Moreover, approximately ** percent of the respondents said that thier company has an automated technology system in place for this purpose.

The Cognitive Data Management market has emerged as a transformative force in the realm of data analytics, revolutionizing how organizations handle, store, and interpret their data. At its core, cognitive data management leverages advanced technologies such as artificial intelligence (AI), machine learning, and natu

The Data Management Technology Application Software market has emerged as a crucial component in today's data-driven landscape, where organizations grapple with the exponential growth of data. This software aids businesses in effectively storing, managing, and analyzing their vast datasets, thereby empowering them t

Financial overview and grant giving statistics of Data Management Association International - Rocky Mountain Cha

The global clinical data management systems market size was valued at USD 1,837.50 million in 2023 and it is expected to grow to USD 4,490.53 million by 2031 at a CAGR of 13.6%.

Financial overview and grant giving statistics of Data Management Association of New England Inc.

Financial overview and grant giving statistics of Data Management Association International-Midsouth Chapter Inc.

In a 2024 survey, over half of the respondents stated that their desired data management benefit on Kubernetes was high availability and disaster recovery for critical Kubernetes applications. Moreover, **** of the organizations would benefit from unified platform for containers and VMs.

The Product Data Management (PDM) Software market has emerged as a critical component in modern enterprises, facilitating the efficient management of product-related data throughout the entire lifecycle. This software suite serves industries ranging from manufacturing to retail, allowing organizations to create, sto

Learning Goals: • explain importance of data management • identify elements of an organized data sheet • create & manipulate data in a spreadsheet • calculate vital statistics using life tables • collect, manage and analyze data to test hypotheses