This data dictionary describes the coding system applied to the data extracted from systematic reviews included in the paper:

Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2023. Statistical synthesis methods other than meta-analysis are commonly used but seldom specified: survey of systematic reviews of interventions

Associated files: 1. Synthesis methods data file: Cumpston_et_al_2023_other_synthesis_methods.xlsx (https://doi.org/10.26180/20785396) 2. Synthesis methods Stata code: Cumpston_et_al_2023_other_synthesis_methods.do (https://doi.org/10.26180/20786251) 3. Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

This file includes the data extracted and coded from systematic reviews included in the paper:

"Cumpston MS, Brennan SE, Ryan R, Thomas J, McKenzie JE. 2023. Synthesis questions are incompletely reported: survey of systematic reviews"

Data dictionary: PICO for synthesis data dictionary (https://doi.org/10.26180/23598933)

Analysis code: PICO for synthesis Stata code: Cumpston_et_al_2023_PICO.do (https://doi.org/10.26180/23597073)

Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.

This file includes the data extracted and coded from systematic reviews included in the paper: "Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2023. Statistical synthesis methods other than meta-analysis are commonly used but seldom specified: survey of systematic reviews of interventions"

Associated files: 1. Synthesis methods data dictionary (https://doi.org/10.26180/20785948) 2. Synthesis methods Stata code: Cumpston_et_al_2023_other_synthesis_methods.do (https://doi.org/10.26180/20786251) 3. Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.

Cross-electrophile coupling (XEC), defined by us as the cross-coupling of two different σ-electrophiles that is driven by catalyst reduction, has seen rapid progression in recent years. As such, this review aims to summarize the field from its beginnings up until mid-2023 and to provide comprehensive coverage on synthetic methods and current state of mechanistic understanding. Chapters are split by type of bond formed, which include C(sp3)–C(sp3), C(sp2)–C(sp2), C(sp2)–C(sp3), and C(sp2)–C(sp) bond formation. Additional chapters include alkene difunctionalization, alkyne difunctionalization, and formation of carbon-heteroatom bonds. Each chapter is generally organized with an initial summary of mechanisms followed by detailed figures and notes on methodological developments and ending with application notes in synthesis. While XEC is becoming an increasingly utilized approach in synthesis, its early stage of development means that optimal catalysts, ligands, additives, and reductants are still in flux. This review has collected data on these and various other aspects of the reactions to capture the state of the field. Finally, the data collected on the papers in this review is offered as Supporting Information for readers.

Space-Based Synthetic Data for AI Training Market Outlook

According to our latest research, the global market size for Space-Based Synthetic Data for AI Training reached USD 1.86 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 27.4% from 2025 to 2033, ultimately reaching USD 17.16 billion by 2033. This remarkable growth is driven by the increasing demand for high-fidelity, scalable, and cost-effective data solutions to power advanced AI models across multiple sectors, including autonomous systems, Earth observation, and defense. As per our latest research, the surge in space-based sensing technologies and the proliferation of AI-driven applications are key factors propelling market expansion.

One of the primary growth factors for the Space-Based Synthetic Data for AI Training market is the exponential increase in the complexity and volume of data required for training sophisticated AI models. Traditional data acquisition methods, such as real-world satellite imagery or sensor data collection, often face challenges related to cost, coverage, and privacy. Synthetic data, generated via advanced simulation techniques and space-based platforms, offers a scalable and customizable alternative. This approach enables AI developers to overcome the limitations of scarce or sensitive datasets, enhancing the robustness of AI algorithms in mission-critical domains like autonomous vehicles, defense, and remote sensing. The ability to generate diverse and unbiased datasets is particularly valuable for training AI systems that must perform reliably under a wide range of conditions, further fueling market growth.

Another significant driver is the rapid advancement in satellite technology and the increasing deployment of small satellites and sensor arrays in low Earth orbit (LEO). These advancements have democratized access to space-based data, making it more feasible for organizations to generate synthetic datasets tailored to specific AI training needs. The integration of high-resolution imagery, multi-spectral sensors, and real-time telemetry from space assets has enabled the creation of synthetic environments that closely mimic real-world scenarios. This, in turn, accelerates the development and deployment of AI-powered applications in sectors such as geospatial intelligence, telecommunications, and disaster management. The synergy between satellite innovation and AI-driven data synthesis is expected to remain a cornerstone of market expansion throughout the forecast period.

Furthermore, regulatory and ethical considerations are playing a pivotal role in shaping the market landscape. With increasing scrutiny over data privacy, especially in sectors like defense and healthcare, organizations are turning to synthetic data as a means to comply with stringent regulations while still harnessing the power of AI. Synthetic datasets generated from space-based sources can be engineered to remove personally identifiable information and sensitive attributes, mitigating compliance risks and fostering innovation. This trend is particularly pronounced in regions with robust data protection frameworks, such as Europe and North America, where organizations are proactively investing in synthetic data solutions to balance compliance and competitive advantage.

From a regional perspective, North America continues to lead the Space-Based Synthetic Data for AI Training market, driven by a strong ecosystem of AI research, space technology innovation, and defense investments. Europe is following closely, buoyed by initiatives in satellite deployment and data privacy regulations that encourage the adoption of synthetic data solutions. Meanwhile, the Asia Pacific region is experiencing rapid growth, propelled by government investments in space programs, smart cities, and AI-driven industrial transformation. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as local industries begin to recognize the benefits of synthetic data for AI training in areas such as agriculture, security, and telecommunications.

This Stata .do file provides the code used to analyse the data extracted and coded from systematic reviews included in the paper: Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2023. Statistical synthesis methods other than meta-analysis are commonly used, but are seldom specified: a survey of systematic reviews of interventions Input file: Synthesis methods data file: Cumpston_et_al_2023_other_synthesis_methods.xlsx (https://doi.org/10.26180/20785396) Associated file: Synthesis methods data dictionary (https://doi.org/10.26180/20785948) Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.

The human auditory system extracts meaning from the environment by transforming acoustic input signals into semantic categories. Specific acoustic features give rise to distinct categorical percepts, such as speech or music, and to spatially distinct preferential responses in the auditory cortex. These responses contain category-relevant information, yet their representational level and role within the acoustic-to-semantic transformation process remain unclear. We combined neuroimaging, a deep neural network, a brain-based sound synthesis, and psychophysics to identify the sound features that are internally represented in the speech- and music-selective human auditory cortex and test their functional role in sound categorization. We found that the synthetized sounds exhibit unnatural features distinct from those normally associated with speech and music, yet they elicit categorical cortical and behavioral responses resembling those of natural speech and music. Our findings provide new insights into the fundamental sound features underlying speech and music categorization in the human auditory cortex.

This file includes the data extracted and coded from systematic reviews included in the paper: "Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2022. Methods for synthesis when meta-analysis is not used: a survey of current practice in systematic reviews of public health and health systems interventions."

Associated files: 1. Methods for synthesis when meta-analysis is not used: Data dictionary (https://doi.org/10.26180/20785948) 2. Methods for synthesis when meta-analysis is not used: Stata code: Cumpston_et_al_2022_other_synthesis_methods 220902.do (https://doi.org/10.26180/20786251) 3. Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. F1000Research 2021, 9:678. (https://doi.org/10.12688/f1000research.24469.2)

Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.

This dataset is associated with the paper 'Artificial Personality and Disfluency' by Mirjam Wester, Matthew Aylett, Marcus Tomalin and Rasmus Dall published at Interspeech 2015, Dresden. The focus of this paper is artificial voices with different personalities. Previous studies have shown links between an individual's use of disfluencies in their speech and their perceived personality. Here, filled pauses (uh and um) and discourse markers (like, you know, I mean) have been included in synthetic speech as a way of creating an artificial voice with different personalities. We discuss the automatic insertion of filled pauses and discourse markers (i.e., fillers) into otherwise fluent texts. The automatic system is compared to a ground truth of human ``acted' filler insertion. Perceived personality (as defined by the big five personality dimensions) of the synthetic speech is assessed by means of a standardised questionnaire. Synthesis without fillers is compared to synthesis with either spontaneous or synthetic fillers. Our findings explore how the inclusion of disfluencies influences the way in which subjects rate the perceived personality of an artificial voice.

This digital dataset compiles a 3-layer geologic model of the conterminous United States by mapping the altitude of three surfaces: land surface, top of bedrock, and top of basement. These surfaces are mapped through the compilation and synthesis of published stratigraphic horizons from numerous topical studies. The mapped surfaces create a 3-layer geologic model with three geomaterials-based subdivisions: unconsolidated to weakly consolidated sediment; layered consolidated rock strata that constitute bedrock, and crystalline basement, consisting of either igneous, metamorphic, or highly deformed rocks. Compilation of subsurface data from published reports involved standard techniques within a geographic information system (GIS) including digitizing contour lines, gridding the contoured data, sampling the resultant grids at regular intervals, and attribution of the dataset. However, data compilation and synthesis is highly dependent on the definition of the informal terms “bedrock” and “basement”, terms which may describe different ages or types of rock in different places. The digital dataset consists of a single polygon feature class which contains an array of square polygonal cells that are 2.5 km m in x and y dimensions. These polygonal cells multiple attributes including x-y location, altitude of the three mapped layers at each x-y location, the published data source from which each surface altitude was compiled, and an attribute that allows for spatially varying definitions of the bedrock and basement units. The spatial data are linked through unique identifiers to non-spatial tables that describe the sources of geologic information and a glossary of terms used to describe bedrock and basement type.

The anthropometric datasets presented here are virtual datasets. The unweighted virtual dataset was generated using a synthesis and subsequent validation algorithm (Ackermann et al., 2023). The underlying original dataset used in the algorithm was collected within a regional epidemiological public health study in northeastern Germany (SHIP, see Völzke et al., 2022). Important details regarding the collection of the anthropometric dataset within SHIP (e.g. sampling strategy, measurement methodology & quality assurance process) are discussed extensively in the study by Bonin et al. (2022). To approximate nationally representative values for the German working-age population, the virtual dataset was weighted with reference data from the first survey wave of the Study on health of adults in Germany (DEGS1, see Scheidt-Nave et al., 2012). Two different algorithms were used for the weighting procedure: (1) iterative proportional fitting (IPF), which is described in more detail in the publication by Bonin et al. (2022), and (2) a nearest neighbor approach (1NN), which is presented in the study by Kumar and Parkinson (2018). Weighting coefficients were calculated for both algorithms and it is left to the practitioner which coefficients are used in practice. Therefore, the weighted virtual dataset has two additional columns containing the calculated weighting coefficients with IPF ("WeightCoef_IPF") or 1NN ("WeightCoef_1NN"). Unfortunately, due to the sparse data basis at the distribution edges of SHIP compared to DEGS1, values underneath the 5th and above the 95th percentile should be considered with caution. In addition, the following characteristics describe the weighted and unweighted virtual datasets: According to ISO 15535, values for "BMI" are in [kg/m2], values for "Body mass" are in [kg], and values for all other measures are in [mm]. Anthropometric measures correspond to measures defined in ISO 7250-1. Offset values were calculated for seven anthropometric measures because there were systematic differences in the measurement methodology between SHIP and ISO 7250-1 regarding the definition of two bony landmarks: the acromion and the olecranon. Since these seven measures rely on one of these bony landmarks, and it was not possible to modify the SHIP methodology regarding landmark definitions, offsets had to be calculated to obtain ISO-compliant values. In the presented datasets, two columns exist for these seven measures. One column contains the measured values with the landmarking definitions from SHIP, and the other column (marked with the suffix "_offs") contains the calculated ISO-compliant values (for more information concerning the offset values see Bonin et al., 2022). The sample size is N = 5000 for the male and female subsets. The original SHIP dataset has a sample size of N = 1152 (women) and N = 1161 (men). Due to this discrepancy between the original SHIP dataset and the virtual datasets, users may get a false sense of comfort when using the virtual data, which should be mentioned at this point. In order to get the best possible representation of the original dataset, a virtual sample size of N = 5000 is advantageous and has been confirmed in pre-tests with varying sample sizes, but it must be kept in mind that the statistical properties of the virtual data are based on an original dataset with a much smaller sample size.

This digital GIS dataset and accompanying nonspatial files synthesize model outputs from a regional-scale volumetric 3D geologic model that portrays the generalized subsurface geology of the Michigan Basin region of Michigan, Wisconsin, Illinois, Indiana, and Ohio. The intent of this product is to rapidly and efficiently synthesize large quantities of geologic data from a wide variety of sources into a multi-use geologic framework model applicable to natural resource exploration and management. Major geographic features within the study area include Lake Michigan and the Lower Peninsula of Michigan, and portions of the Upper Peninsula of Michigan, Lake Huron, Lake Saint Clair, and Lake Erie. Geologically, the study area incorporates major structures such as the Kankakee Arch, Findlay Arch, Wisconsin Arch, Algonquin Arch, Lake Superior Syncline, and the broader Michigan sedimentary basin from the Precambrian basement to the Earth's surface. Data released here consists of stratigraphic horizon grids of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, a stratigraphic horizon grid of the uppermost Precambrian basement rocks, and line data that estimate the two-dimensional geometry of fault planes that intersect stratigraphic horizon grids. The presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of private and public surface and subsurface geologic data; none of these input data are part of this data release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. The MichiganBasin3D geodatabase contains 41 subsurface stratigraphic horizons in raster format, representing the tops of modeled subsurface units, as well as a feature dataset titled "GeologicModel." This feature dataset includes a line feature class of 180 fault segments that delineate faults with probable post-Precambrian offset extending into the Phanerozoic sedimentary layers (PrecambrianLevelFaults_Throughgoing). It also features a line class of 170 fault segments representing throughgoing faults at the level of the top Trenton Group (TrentonLevelFaults), along with a polygon feature class delineating the study area (ModelBoundary). Nonspatial tables provide definitions of data sources (DataSources), terminology used in the dataset (Glossary), and descriptions of the modeled surfaces (DescriptionOfModelUnits). Additional file folders contain vector data in shapefile format, raster data in ASCII format, and tables in comma-separated values format. A comprehensive data dictionary outlines the entity and attribute information for all geospatial data and accompanying nonspatial tables (EntityAndAttributes). Accompanying this data release is a workbook “MichiganBasinInputSummaryTable.csv”, which tabulates the stratigraphic horizons modeled in each fault block, and the types and quantity of data inputs for each stratigraphic horizon. Key references associated with each data input are found in the "MichiganBasinInputReferences" so any user could replicate this work if they desired. An associated USGS report: “A three-dimensional geologic framework model of the Michigan Basin region, Illinois, Indiana, Michigan, Ohio, and Wisconsin, USA” documents the process of manipulating and interpreting surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions.

Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).

The objective was to identify innovative strategies that may increase recruitment and/or retention of groups less represented in chronic disease clinical research. A systematic review was conducted. Inclusion criteria were: (a) NIH-defined racial and ethnic minority groups and clinical research; (b) evidence-based, clinical research recruitment and/or retention strategies involving the leading causes of mortality and morbidity in the United States; (c) conducted in the United States; and (d) qualitative design. Data exploring the strategies were extracted and thematically analyzed. Twenty-seven studies were included. Studies focused on cancer (70%), recruitment (93%), and perspectives from clinicians (63%). The most referenced strategies were education (44%), communication (48%), and community-based participatory research (63%). Critical themes include empowerment, transparency, trust, and sustainability. Strategies must prioritize the community and be implemented sustainably, where cultural humility and community-based participatory research are foundational. Methods We adhered to and adapted the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines, Enhancing the Quality and Transparency of Health Research (EQUATOR) guidelines, and The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist to report this completed systematic review and meta-synthesis. Eligibility criteria Briefly, we included studies with qualitative or mixed methods research designs, were conducted in the U.S., published in English or Spanish in a peer-reviewed journal between 2009 and 2024, and that demonstrated evidence-based recruitment and/or retention strategies for clinical research focused on the leading causes of morbidity and mortality in the U.S. as determined by the Centers for Disease Control and Prevention (CDC). Studies spanning ten years from 2009 were initially selected; this was later updated to include fifteen years from 2009 to reflect the increasing significance and importance of this work during this period. Eligible studies targeted ethnic and racial minorities defined by the NIH. Studies included all ages and used the NIH definition of clinical research. Information sources and search strategy The literature search strategy was developed in collaboration with the review team and trained biomedical librarians (NT and AL) at the National Institutes of Health (NIH). The search strategy was created using a combination of text words and the controlled vocabulary terms in the following databases: (PubMed (MeSH) Medical Subject Headings, Embase - EMTREE, and CINAHL subject headings. The search was refined using an iterative process and finalized by the review team members and librarians. For each search strategy, the search terms included these text words and controlled vocabulary when available: underrepresented, minority, racial and ethnic groups, clinical research, and disparities. The following databases were searched: PubMed (National Library of Medicine), Embase (Elsevier), CINAHL Plus (Cumulative Index to Nursing and Allied Health Literature - EBSCOhost), and Web of Science Core Collection (Clarivate Analytics). The following limits were applied using the filters available in each database. The search was limited to human studies only and was limited to studies conducted in the United States. The final search strategy can be found in the S2 Appendix. Selection process Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia; www.covidence.org) imported studies and automatically excluded duplicates. All stages of the screening, data extraction, and quality assessment were independently conducted by members of the review team (CJP, JMG, AK, MW, LA, and JGG). The review team was composed of six members. The review team first screened titles and abstracts to identify studies that met the inclusion criteria. Next, the full texts of studies included during the title and abstract were screened using the same eligibility criteria. Each article was screened by two reviewers and conflicts between reviewers were resolved by consensus discussion with the review team. Data collection process & data items Data from each included study were collected by two reviewers using Covidence. The following outcomes of interest were extracted: focus on recruitment, retention, or both; and a description of the evidence-based strategies. We extracted data on study characteristics, including year of publication and condition of interest, including subtype for cancer. Additionally, we extracted characteristics including race and ethnicity, sex assigned at birth if applicable, geography (urban or rural), and role in clinical research (e.g., participant, clinician (i.e., medical or research staff), community leader, etc.). Study risk of bias assessment For the quality assessment, we evaluated the following domains: (a) role of the researcher; (b) sampling method; (c) data collection method; and (d) analysis method, which were identified as all criteria met or criteria partially met. We followed the adapted guidelines and conceptual domains of the Critical Appraisal Skills Programme (CASP) quality assessment tool to assess the quality of studies. Two reviewers assessed the risk of bias for each included study and resolved disagreements by consensus discussion with the review team. Synthesis methods A thematic synthesis was operationalized for the data analysis, where we analyzed the findings and developed inductive and deductive codes using qualitative synthesis methodologies and established guidelines. The thematic synthesis utilized an iterative process grounded in qualitative thematic analysis methodologies. We initially developed a deductive coding scheme, focusing on direct meaning and content that highlighted evidence-based strategies and direct quotations from study participants. The team discussed and created the codebook, and then each code was defined. Discrepancies or additional deductive codes were added and discussed by the team for consensus. Each study was coded independently by reviewers. Inductive codes were later added to describe high-level interpretation and themes. Both deductive and inductive codes existed in our codebook using this iterative process. Further analysis was conducted where strategies and themes were summarized into a conceptual model emphasizing key elements for the recruitment and retention of racial and ethnic groups historically underrepresented in clinical research.

5-endo-trig Radical Cyclizations:  A New Means to the Stereoselective Synthesis of Cyclopentanes and Diquinanes

A palladium-catalyzed C–H bond functionalization of acrylamides was developed to build up stereoselectively trifluoromethylated 1,3-butadienes. Using a tertiary amide as a directing group, olefins were selectively functionalized with 2-bromo-3,3,3-trifluoropropene to access these important fluorinated compounds. The methodology was extended to the construction of pentafluoroethyl-substituted 1,3-dienes. Mechanistic studies supported by density functional theory calculations suggested a redox neutral mechanism for this transformation.

IntroductionFoundational to a well-functioning health system is a strong routine health information system (RHIS) that informs decisions and actions at all levels of the health system. In the context of decentralization across low- and middle-income countries, RHIS has the promise of supporting sub-national health staff to take data-informed actions to improve health system performance. However, there is wide variation in how “RHIS data use” is defined and measured in the literature, impeding the development and evaluation of interventions that effectively promote RHIS data use.MethodsAn integrative review methodology was used to: (1) synthesize the state of the literature on how RHIS data use in low- and middle-income countries is conceptualized and measured; (2) propose a refined RHIS data use framework and develop a common definition for RHIS data use; and (3) propose improved approaches to measure RHIS data use. Four electronic databases were searched for peer-reviewed articles published between 2009 and 2021 investigating RHIS data use.ResultsA total of 45 articles, including 24 articles measuring RHIS data use, met the inclusion criteria. Less than half of included articles (42%) explicitly defined RHIS data use. There were differences across the literature whether RHIS data tasks such as data analysis preceded or were a part of RHIS data use; there was broad consensus that data-informed decisions and actions were essential steps within the RHIS data use process. Based on the synthesis, the Performance of Routine Information System Management (PRISM) framework was refined to specify the steps of the RHIS data use process.ConclusionConceptualizing RHIS data use as a process that includes data-informed actions emphasizes the importance of actions in improving health system performance. Future studies and implementation strategies should be designed with consideration for the different support needs for each step of the RHIS data use process.

The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP released PAD-US 3.0 Statistics and Reports in the summer of 2022. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 3.0 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 51,000 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.

IntroductionFoundational to a well-functioning health system is a strong routine health information system (RHIS) that informs decisions and actions at all levels of the health system. In the context of decentralization across low- and middle-income countries, RHIS has the promise of supporting sub-national health staff to take data-informed actions to improve health system performance. However, there is wide variation in how “RHIS data use” is defined and measured in the literature, impeding the development and evaluation of interventions that effectively promote RHIS data use.MethodsAn integrative review methodology was used to: (1) synthesize the state of the literature on how RHIS data use in low- and middle-income countries is conceptualized and measured; (2) propose a refined RHIS data use framework and develop a common definition for RHIS data use; and (3) propose improved approaches to measure RHIS data use. Four electronic databases were searched for peer-reviewed articles published between 2009 and 2021 investigating RHIS data use.ResultsA total of 45 articles, including 24 articles measuring RHIS data use, met the inclusion criteria. Less than half of included articles (42%) explicitly defined RHIS data use. There were differences across the literature whether RHIS data tasks such as data analysis preceded or were a part of RHIS data use; there was broad consensus that data-informed decisions and actions were essential steps within the RHIS data use process. Based on the synthesis, the Performance of Routine Information System Management (PRISM) framework was refined to specify the steps of the RHIS data use process.ConclusionConceptualizing RHIS data use as a process that includes data-informed actions emphasizes the importance of actions in improving health system performance. Future studies and implementation strategies should be designed with consideration for the different support needs for each step of the RHIS data use process.

This digital GIS dataset and accompanying nonspatial files synthesize the model outputs from a regional-scale volumetric 3-D geologic model that portrays the generalized subsurface geology of western South Dakota from a wide variety of input data sources.The study area includes all of western South Dakota from west of the Missouri River to the Black Hills uplift and Wyoming border. The model data released here consist of the stratigraphic contact elevation of major Phanerozoic sedimentary units that broadly define the geometry of the subsurface, the elevation of Tertiary intrusive and Precambrian basement rocks, and point data representing the three-dimensional geometry of fault surfaces. the presence of folds and unconformities are implied by the 3D geometry of the stratigraphic units, but these are not included as discrete features in this data release. The 3D geologic model was constructed from a wide variety of publicly available surface and subsurface geologic data; none of these input data are part of this Data Release, but data sources are thoroughly documented such that a user could obtain these data from other sources if desired. This model was created as part of the U.S. Geological Survey’s (USGS) National Geologic Synthesis (NGS) project—a part of the National Cooperative Geologic Mapping Program (NCGMP). The WSouthDakota3D geodatabase contains twenty-five (25) subsurface horizons in raster format that represent the tops of modeled subsurface units, and a feature dataset “GeologicModel”. The GeologicModel feature dataset contains a feature class of thirty-five (35) faults served in elevation grid format (FaultPoints). The feature class “ModelBoundary” describes the footprint of the geologic model, and was included to meet the NCGMP’s GeMS data schema. Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfModelUnits). Separate file folders contain the vector data in shapefile format, the raster data in ASCII format, and the nonspatial tables as comma-separated values. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). An included READ_ME file documents the process of manipulating and interpreting publicly available surface and subsurface geologic data to create the model. It additionally contains critical information about model units, and uncertainty regarding their ability to predict true ground conditions. Accompanying this data release is the “WSouthDakotaInputSummaryTable.csv”, which tabulates the global settings for each fault block, the stratigraphic horizons modeled in each fault block, the types and quantity of data inputs for each stratigraphic horizon, and then the settings associated with each data input.