As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

As of June 2024, the most popular commercial database management system (DBMS) in the world was Oracle, with a ranking score of 1244. MySQL was the most popular open source DBMS at that time, with a ranking score of 1061.

The global database management system (DBMS) market revenue grew to 80 billion U.S. dollars in 2020. Cloud DBMS accounted for the majority of the overall market growth, as database systems are migrating to cloud platforms.

Database market

The database market consists of paid database software such as Oracle and Microsoft SQL Server, as well as free, open-source software options like PostgreSQL and MongolDB. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Database management software

Knowledge of the programming languages related to these databases is becoming an increasingly important asset for software developers around the world, and database management skills such as MongoDB and Elasticsearch are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

In 2023, over 45 percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the database tech stack in 2023 was MySQL, chosen by more than half of respondents. It was followed by PostgreSQL, while NoSQL ranked fifth, chosen by only 4.5 percent of respondents.

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

Description of the database:

• Objectives and initial purposes of the database:

This synthetic dataset was created as part of the translation and implementation of the algorithm used by the CNAM to build the top diabetes (link to the description sheet of the algorithm).

The Python and SAS versions adapted by the HDH cover synthetic data for the years 2018-2019 but can be extended to other years. The CNAM source program was developed in SAS and runs on data from 2015 to 2019.

The objective of the algorithm mentioned above is to target people in care for diabetes in the main base of the NSDS in order to create the ‘Top Diabetes’ of the pathology mapping created and maintained by the CNAM (version G8).

• Context of creation:

The implementation of the top diabetes algorithm required the mobilization of synthetic (fictitious) tables and variables.

-merge annual tables into a single table for ER_PRS_F, ER_ETE_F, ER_PHA_F,

Data/SNDS community.

• Results associated with the creation of the database:

The algorithm used by the CNAM to construct the top diabetes: (source version (CNAM), Python version and SAS version (HDH)) (https://www.health-data-hub.fr/library-open-algorithms-health/algorithm-to-build-the-top-diabete-of-mapping).

Recruit people, in a wide variety of fields, to work in Quebec, which is looking to recruit in the region.

• Collection methodology and inclusion criteria:

Data presentation:

The programmes operate on the synthetic data of the HDH with some adaptations: This dataset was generated using the scheme of the 2019 NSDS main database tables. - Target audience: -the conversion of the date format to yymmdd10.

Patient identification is based on the targeting of specific medicines and/or ALD and/or hospitalisation in MCO.

-the renaming of NUM_ENQ to BEN_NIR_PSA, The mapping algorithms aim to maximize specificity (not sensitivity), i.e. to ensure the absence of non-diabetics among the targeted patients.

• Choice of variables:

The implementation of the algorithm requires the mobilisation of the following tables and variables (the required history is indicated in the corresponding box): Patients with less than 3 dispensings of specific drugs, who do not have ALD and who have not been hospitalized within 5 years for diabetes are not retained.

The programs adapted in SAS and Python run on synthetic data from the years 2018 and 2019. The CNAM source code (in SAS) was designed to work on data from the years 2015 to 2019.

Limits of this dataset:

https://gitlab.com/healthdatahub/boas/cnam/top-diabete/-/raw/main/Tables_et_variables_du_SNDS_n%C3%A9cessaires.png?ref_type=heads" alt="enter image description here" title="enter image title here"> the lack of medical consistency, the lack of updating of annual changes, an evolutionary table scheme that can be incomplete and imperfect.

This programme does not include an analysis of the estimated items of expenditure reimbursed by Health Insurance.

The algorithm identifies prevalent patients with diabetes in a given year (2019). It does not determine the exact date of onset of diabetes in the base.

The use of synthetic data, although useful for manipulating NSDS data, has limitations:

More information on the use of the database in the context of the top diabetes programmes (CNAM) on the GitLab repository of the programmes (link of the GitLab repository).

Support:

Contact point: dir.donnees-SNDS@health-data-hub.fr

Contribution:

On Gitlab (make a ticket or merge-request)

The Alaska Geochemical Database Version 4.0 (AGDB4) contains geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving efficiency of use. The relational database includes historical geochemical data archived in the USGS National Geochemical Database (NGDB), the Atomic Energy Commission National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance databases, and the Alaska Division of Geological and Geophysical Surveys (DGGS) Geochemistry database. Data from the U.S. Bureau of Mines and the U.S. Bureau of Land Management are included as well. The data tables describe historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 120 laboratory and field analytical methods performed on 416,333 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. The samples were collected as part of various agency programs and projects from 1938 through 2021. Most samples were collected by agency personnel and analyzed in agency laboratories or under contracts in commercial analytical laboratories. Mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are also included in this database. The data in the AGDB4 supersede data in the AGDB, AGDB2, and AGDB3 databases but the background about the data in these earlier versions is needed to understand what has been done to amend, clean up, correct, and format these data. Data that were not included in previous versions because they predate the earliest agency geochemical databases or were excluded for programmatic reasons are included here in the AGDB4. The AGDB4 data are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. They are provided as a Microsoft Access database, as comma-separated values (CSV), and as an Esri geodatabase consisting of point feature classes and related tables.

Users can search for model or promising programs related to community health programs and initiatives. Topics include: access to care, health equity, immunization, mental health, primary care, cultural competence, and environmental health. BackgroundThe Model Practice Database is maintained by the National Association of County and City Health Officials (NACCHO). This database allows users to search for model or promising programs related to community health programs and initiatives. Topics include, but are not limited to: access to care, health equity, immunization, mental health, primary care, cultural competence, and environmental health. User FunctionalityUsers can search for model or promising programs by state or category. Users can view details regarding model programs, contact information for the respective program, and a link to the program webpage, if applicable. Data NotesThe program overview, program details (i.e., agency and community roles, innovation, expenditures, implementation, sustainability and lessons learned), and program contact information are provided for each model program. The year in which the program overview was submitted is indicated with the program summary.Most recent program overviews were submitted in 2009. Programs throughout the United States are included.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the .NET C# tech stack in 2023 was database connectivity, chosen by over 15 percent of respondents. It was followed by MVC and REST in the second and fourth place, respectively.

About TMDb (The Movie Database)

The Movie Database (TMDb) is a comprehensive, community-driven movie and TV database. Launched in 2008, TMDb has become one of the most popular sources of movie and TV show information globally. It provides data about movies, TV shows, actors, directors, and other film industry professionals. TMDb’s platform allows users to explore movie details, browse recommendations, and view user-generated ratings and reviews.

One of TMDb's key strengths is its API, which developers can use to access its vast database. The API provides access to a wide range of data, including movie metadata (titles, release dates, overviews), images, videos, and even user ratings. TMDb’s API is extensively used in various applications, including movie recommendation systems, content discovery platforms, and entertainment apps.

About the Dataset

The dataset we are working with is derived from the TMDb API and focuses on Top-Rated Movies. This dataset contains key information about movies that have received high ratings from users on TMDb. It includes essential movie metadata such as the movie ID, title, release date, overview (a brief description of the movie), popularity score, average user rating, and the number of votes each movie has received.

The data is structured to be easily accessible and useful for analysis, making it a valuable resource for anyone interested in exploring trends in popular cinema, understanding what makes a movie highly rated, or simply discovering new films to watch.

Purpose of the Dataset

The purpose of this dataset is to provide insights into the characteristics of top-rated movies. By analyzing this dataset, one can:

1. Identify Trends: Understand how movie preferences and ratings have evolved over time. For example, one might explore how the average rating of movies has changed over the years or identify which genres are most commonly associated with high ratings.

2. Explore Popularity vs. Ratings: Investigate the relationship between a movie's popularity and its average rating. This can help in understanding whether highly popular movies tend to receive better ratings or if there's a disconnect between popularity and quality.

3. Content Analysis: Use the overview text to perform sentiment analysis or keyword extraction to understand common themes in highly-rated movies.

4. User Behavior: Analyze voting patterns, such as how many votes a movie needs before it reaches a certain rating threshold, or how ratings differ between movies with a high number of votes versus those with fewer votes.

5. Recommendation Systems: Leverage the dataset to build or enhance movie recommendation systems by identifying patterns and similarities among top-rated movies.

Overall, this dataset is a powerful tool for anyone looking to dive deep into movie data, whether for academic research, application development, or personal exploration of film trends.

Attributes in the Dataset

To create a new DataFrame from the API data, we will focus on extracting specific fields that are crucial for analyzing and understanding the top-rated movies.

These fields are:

• id: This is a unique identifier for each movie. It helps in distinguishing one movie from another and can be used for referencing specific movies in subsequent operations.

• title: The title of the movie. This field provides the name of the movie, which is essential for identifying and understanding the content of the dataset.

• release_date: The date when the movie was released. This information is valuable for analyzing trends over time, understanding the distribution of movies across different years, and identifying the most recent top-rated movies.

• overview: A brief summary or description of the movie. This field provides context about the movie's plot and can be used for more in-depth content analysis, such as sentiment analysis or keyword extraction.

• popularity: A numeric value representing the movie's popularity. This field is important for understanding how well-received or widely recognized the movie is. Popularity can be influenced by various factors such as marketing, star power, and public reception.

• vote_average: The average rating given by users. This field is crucial for identifying the quality or critical reception of the movie. Higher vote averages typically indicate better-received movies.

• vote_count: The number of votes that contributed to the vote average. This field provides context to the vote average by indicating how many users rated the movie. A higher vote count can lend more credibility to the vote average.

This data set contains U.S. station surface observations that were digitized from the original forms by the Climate Database Modernization Program (CDMP). Data are available for more than 200 stations (mainly at airports, but also some city weather bureau offices) that made observations at hourly intervals at least during the daytime hours and often over the full 24-hour day. The general period of record for these stations is 1928-1948, though this varies by individual station. To find out what is available, see this inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/sao_inventory.txt]. A significant effort was made by DSS to correct errors in the digitized data, especially dates, times, and pressures. For more information about this work, see this document [https://rda.ucar.edu/datasets/ds506.0/docs/qc-20040110.txt]. We have also received data for more than 130 U.S. stations that were digitized from Form 1001. These stations, which were usually city weather bureau offices, generally took observations once, twice, or four times daily. Some stations have data back as far as late 1892. An inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/form1001_inventory.txt] can be viewed which shows stations and their date range coverage. These data will be made available to the community when errors in the digitized data have been corrected.

Issued: 2018-11-14

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from databases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these databases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of the AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

We recently introduced transformato, an open-source Python package for the automated setup of large-scale calculations of relative solvation and binding free energy differences. Here, we extend the capabilities of transformato to the calculation of absolute solvation free energy differences. After careful validation against the literature results and reference calculations with the PERT module of CHARMM, we used transformato to compute absolute solvation free energies for most molecules in the FreeSolv database (621 out of 642). The force field parameters were obtained with the program cgenff (v2.5.1), which derives missing parameters from the CHARMM general force field (CGenFF v4.6). A long-range correction for the Lennard-Jones interactions was added to all computed solvation free energies. The mean absolute error compared to the experimental data is 1.12 kcal/mol. Our results allow a detailed comparison between the AMBER and CHARMM general force fields and provide a more in-depth understanding of the capabilities and limitations of the CGenFF small molecule parameters.

Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which—and to what extent—these concepts are included in these integrated experiences, especially when compared to concepts commonly taught in introductory computer science courses. Thus, it is unclear how integrated computing activities serve the goal of broadening participation in computing. To address this deficit, we compiled a database of 81 integrated computing activities, constructed a framework of fundamental programming concepts, and scored each activity in the database for the presence of each concept. The dataset also includes different activity features, including discipline, programming language, student age, and duration of activity. Methods Selection Criteria: Features and Limitations Non-CS Disciplinary Learning Objectives The first selection criterion for activities to include in the analysis was the inclusion of learning objectives in a discipline other than computing. No restrictions were placed on which other disciplines qualified, and we found activities from language arts, math, science, art, music, foreign language, history, social studies, and even spatial skill development for young children. One indirect benefit of requiring non-computing disciplinary learning objectives was that many included activities have substantive lesson plans. These lesson plans make the activities more accessible to teachers by including TPACK-related information, such as disciplinary learning objectives for the activity. As a result, the authors recognize the limitations of requiring non-computing learning objectives but also that it provides a level of authenticity and accessibility for the included activities. One of the major sources of computing integration activities affected by this requirement was the ScratchEd website. Scratch is a popular language for computing integration activities, aided by an extensive repository of student- and teacher-created projects that users are encouraged to remix into their own projects. The thousands of programs in this repository are of widely varying complexity and quality, and most of them are listed with a topic but without explicit learning objectives. To draw from this wealth of activities without comprehensively including projects, we identified lists of vetted computing integrated activities using Scratch to include in the analysis. These lists were "Integrated Scratch Programming in the Curriculum," "Scratch Projects Across the Curriculum," "From Music to Math: Scratch Across Every Subject," and "Scratch Cross-Curricular Integration Guide." Similarly, resources related to the Snap! language had plentiful examples of projects across disciplines with limited explicit non-CS disciplinary learning objectives. Block-Based Programming Languages Because computing integration activities are becoming popular, an initial search revealed too many activities to score in one analysis. To narrow the scope of the analysis, the next selection criterion was that the activity had to use a block-based programming language. This criterion has benefits and limitations. One of the main benefits for the goal of the current analysis was that block-based activities include a range of concepts, regardless of their syntactic or semantic difficulty (Grover & Basu, 2017; Papadakis et al., 2014). This benefit means that concepts that best serve the activity can be included for learners with little to no programming experience (Weintrop & Wilensky, 2018). The associated limitation, however, was that concepts are also restricted by the blocks that are built into the language. Most popular languages use a low-floor, high-ceiling design that includes blocks for all concepts that would be taught in an introductory programming course, though (Grover, 2021; Weintrop & Wilensky, 2015). Another limitation was that prominent, text-based integration activities, such as Bootstrap’s curricula in Algebra and Physics, are excluded. This selection criterion also notably excluded commonly used science simulation platforms, like NetLogo and PhET. These platforms include a large range of simulations for scientific phenomena and other models beyond science. While the simulations allow users to easily access the source code, the primary interface does not include the program used to create the simulation. In addition, the source code, except for some adapted NetLogo simulations, is text-based. Though the programs are heavily commented to make them understandable, they do not meet the inclusion criteria for the current dataset. More programming-centric and block-based options for scientific simulations, like StarLogo Nova, were included. Access Accessibility of the activities was the final criterion for inclusion. Following the accessibility criteria used by Lin and Weintrop (2021), we included activities only if they could be found online, were free of cost, did not require a physical device like robotics toolkits, and were updated recently enough that they ran on current versions of languages and operating systems. The requirement to be found online is not expected to substantially narrow the analysis because Lin and Weintrop found that 90% of block-based programming languages ran in a web browser. Exclusion for use of physical devices is a corollary to the requirement to be free of cost. We felt that these criteria would result in a dataset that had the broadest and most equitable applications because many public schools in low-income areas in the US cannot afford physical computing or robotics kits. Search Criteria Users need to recognize that the current dataset was based on a review of computing integration activities but not a systematic review. Unlike systematic literature reviews of scholarly work on a given keyword or topic area, there were no databases of indexed computing integration activities that span our inclusion criteria. Some repositories for certain languages exist, such as ScratchEd’s repository of Scratch projects and the Exploring Computational Thinking repository of Pencil Code and Python activities. However, computing integration activities are not published through a central organization, so they can be difficult to find. In lieu of a systematic review, we attempted to build a database that represented activities from a variety of disciplines, student ages, designers, and languages. To create this database, we included any activities that we were already aware of, such as Action Fractions, links from lists of computing integration activities, such as "Scratch Projects Across the Curriculum," links from CSforAll’s curriculum directory, and a general Google search for "‘integrated computing’ activities" and "‘computational thinking’ + programming" or "‘computational thinking’ + coding." We examined the first 100 returns for these searches. However, many of the activities found through Google search were excluded based on our criteria, primarily for not including non-CS learning objectives. We included activities as whole units, whether they were single-class lessons or extended curricular units that included multiple lessons, like Coding as Another Language. Treating individual lessons from curricular units as individual activities would have created an over-representation of extended units (e.g., 72 lessons for the Kindergarten, 1st, and 2nd grade curricular units from Coding as Another Language instead of 3 activities). Our database included 81 activities from the following sources: • CANON Lab • Code.org’s CS Connections • Code.org’s Hour of Code • Coding as Another Language curriculum • CS+ units from University of California San Diego • CSforALL’s Curriculum Repository (including 144 curricular units at the time of searching) • CT4Edu • Everyday Computing • Exploring Computational Thinking • Google search • Google’s CS First • Integrated computing activities from Georgia State University • Project GUTS • ScratchEd • The Tech Interactive • TVO Learn • UCL Scratch Maths We analyzed the distribution of these activities’ characteristics based on primary discipline, student age, programming language, and minimum time to complete. Based on discipline, we recognized that we had only two from history or social studies and searched for additional activities. While we found many projects on ScratchEd’s website, they did not meet the selection criteria. Required courses, including Language Arts, Math, and Science had a sufficient number of activities, matching their representation in the school day. We also had a wide range of activities based on student age and minimum time to complete, so we did not search for any additional activities based on these characteristics. To explore the representation in our database based on programming languages, we used the categories identified by Lin and Weintrop (2021) to ensure coverage of different types of block-based languages. The database has activities from Pencil Code (i.e., block-based implementation of a text-based language), Scratch (i.e., multimedia focused on animations and storytelling), AppLab (i.e., mobile app development), StarLogo Nova (i.e., simulations), and ScratchJr (i.e., pre-reading language). We decided against requiring languages from Lin and Weintrop’s other categories for data science, physical computing, and task-specific languages because they did not match our inclusion criteria. We explored other common languages to include, like Alice, Snap!, and App Inventor, but we did not find activities that matched our criteria.

The Alaska Geochemical Database Version 2.0 (AGDB2) contains new geochemical data compilations in which each geologic material sample has one "best value" determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database (AGDB) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future Alaskan geologic and geochemical projects, and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey (USGS) personnel and analyzed in USGS laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various USGS programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the USGS Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the USGS PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. This arduous process of reviewing, verifying and, where necessary, editing all USGS geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest USGS geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.