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, 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.

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.

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.

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

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.

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 synthesis of the stereotriad core in the eastern portion of the Veratrum alkaloids jervine (1), cyclopamine (2), and veratramine (3) is reported. Starting from a known β-methyltyrosine derivative (8), the route utilizes a diastereoselective substrate-controlled 1,2-reduction to establish the stereochemistry of the vicinal amino alcohol motif embedded within the targets. Oxidative dearomatization is demonstrated to be a viable approach for the synthesis of the spirocyclic DE ring junction found in jervine and cyclopamine.

This dataset contains the code and instructions needed to replicate the experimental results presented in the TACAS 2018 paper "Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts".The problem of program synthesis - the definition of processes to automatically derive implementations that are guaranteed to comply with specifications expressed in the form of logic formulas - is an increasingly well-studied area.Here and in the accompanying paper, a novel approach to automate program synthesis using a validity-guided technique and an Assume-Guarantee convention for specifications is demonstrated. This approach is efficient, general and completely automated, with no requirement for the templates or user guidance relied upon by existing techniques such as k-induction.The implementation of this novel algorithm for the synthesis of reactive systems, named JSyn-vg, has been added to a branch of the JKind model checker. This implementation, the benchmarks used to demonstrate its effectiveness as well as other dependencies are provided here. Further details regarding the benchmarks and instructions need to reproduce the results in the accompanying paper are provided in README.txt.

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at https://github.com/river-corridors-sfa/rc_sfa-rc-3-wenas-meta. This data package was originally published in March 2024. It was updated in April 2025more » (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.« less

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.

ABSTRACT This work aims to create an overview of the uses of the term folksonomy in the Literature of Information Science through the analysis of the enunciated concepts. It contributes with a step toward consolidation of the folksonomy concept taking into account definitions used by authors working with the theme and by the creator of this terminology, Thomas Vander Wal. The research’s nature is exploratory and bibliographical based on content analysis and literature review about folksonomy and collaborative representation of information. It points out that there is not a single, clear, well enunciated and rigorous definition about folksonomy. It concludes proposing the concept of Folksonomy as: the result of free labeling process (assignment of tags or keywords) performed by users through the use of terms from the natural language - dispensing the use of controlled vocabularies - in collaborative digital environments aiming to indexing shared information resources of any format (text, images, audio, video, etc.) for purposes of their representation and retrieval.

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.

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.

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.

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.

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).

VGT-S1 products (daily synthesis) are composed of the 'Best available' ground reflectance measurements of all segments received during one day for the entire surface of the Earth. This is done for each of the images covering the same geographical area. The areas distant from the equator have more overlapping parts so the choice for the best pixel will be out of more data. These products provide data from all spectral bands, the NDVI and auxiliary data on image acquisition parameters. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3

Elaborated operational definitions for levels of community engagement in research.