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.

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).

This review seeks to synthesize a definition of general personal self-determination based on the literature and to identify whether the current instruments are suitable to measure general personal self-determination in both neurotypical and neurodivergent populations.

Physical defence investment in seeds varies greatly among plant species and is associated with many potential factors. Exploring the factors explaining the interspecific variation in physical defence has long attracted particular attention in both ecology and evolution studies. However, the relative importance of the factors has not yet been quantitatively evaluated, which may lead to the misunderstanding of the main driver generating such interspecific variation. Here, by compiling a global database of the seed coat ratio (SCR), a proxy of seed physical defence, for 1,362 species, we provided the first quantification of the relative explanations of six factors that have been commonly considered to be associated with the interspecific variation in SCR: seed mass, seed desiccation response (desiccation-sensitive vs. desiccation-tolerant), seed dormancy (nondormant, physical dormant or other dormant types), growth form (herbaceous vs. woody), fruit type (dry vs. fleshy), and climate (19 b..., Data on the SCR and seed mass were collected from the literature published up until June 5, 2022. We used ((diaspore$ or “seed* coat$†or (seed$ and (kernel$ or reserve$)) or (seed$ and “embryo* endosperm$†)) and (ratio$ or proportion$ or fraction$ or tissue$ or percent* or weigh* or mass*)) or (seed$ and (defen* or protect*) and (physical* or mechanical*)) as search terms in the ISI Web of Science and further restricted our search to be consistent with the ‘Study Field Categories’ of Wu et al. (2019), with the additional category ‘anatomy morphology’. Google Scholar was also searched with the same keywords to expand our dataset. Finally, 9,322 journal papers written in English were search out. We first screened all the papers based on the titles and abstracts and excluded 6,857 studies that were not relevant to our focused question. For the remaining 2,465 papers, we screened the full texts and finally yielded 85 papers containing available SCR data collected from 203 sampling sites. T...,

seed desiccation response  1 means desiccation-tolerant, 0 means desiccation-sensitive, blanks mean lack of information.

growth form  1 means woody, and 0 means herbaceous.

fruit type  1 means fleshy, 0 means dry, blanks mean lack of information.

nondormant  1 means nondormant, 0 means dormant, blanks mean lack of information.

physical dormant  1 means physical dormant, 0 means non physical dormant, blanks mean lack of information.

other dormant  1 means other dormant (include physiological dormancy, morphological dormancy and morphophysiological dormancy), 0 means nondormant or physical dormant, blanks mean lack of information.

, # Title of Dataset:

Data from: Disentangling the relative contributions of factors determining seed physical defence: a global-scale data synthesis

Description of the Data and file structure

Dataset of SCR.xlsx Excel spreadsheet which includes species information, seed coat ratio, and factors that have been commonly considered as possible drivers of variation in the SCR: seed mass, seed desiccation response, growth form, fruit type, climate, and seed dormancy (nondormant, physical dormant or other dormant types).

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 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.

RationalePatient satisfaction is a complex construct consisting of human and system attributes. Patient satisfaction can afford insight into patient experience, itself a key component of evaluating healthcare quality. Internationally, advanced physiotherapy practice (APP) extends across clinical fields and is characterised as a higher level of practice with a high degree of autonomy and complex decision making. Patient satisfaction with APP appears positive. While evidence synthesis of patient satisfaction with APP exists, no systematic review has synthesised evidence across clinical fields. Therefore, the objectives of this systematic review are 1) to evaluate patient satisfaction with APP internationally, and 2) to evaluate human and system attributes of patient satisfaction with APP.Materials and methodsA systematic mixed studies review using a parallel-results convergent synthesis design will be conducted. Searches of Medline, Embase, Web of Science, CINAHL, Cochrane, PEDro and grey literature databases will be conducted from inception to 18/7/2023. Studies of APP (World Physiotherapy definition) whereby practitioners a) have advanced clinical and analytical skills that influence service improvement and provide clinical leadership, b) have post-registration masters level specialisation (or equivalence), c) deliver safe, competent care to patients with complex needs and d) may use particular occupational titles; that measure patient satisfaction across all clinical fields and countries will be included. Two reviewers will screen studies, extract data, assess methodological quality of included studies (mixed methods appraisal tool), and contribute to data synthesis. Quantitative data will undergo narrative synthesis (textual descriptions) and qualitative data thematic synthesis (analytical themes). Integration of data syntheses will inform discussion.ImplicationsThis systematic review will provide insight into patient satisfaction with APP internationally, exploring attributes that influence satisfaction. This will aid design, implementation, or improvement of APP and facilitate the delivery of patient-centred, high-quality healthcare. Lastly, this review will inform future methodologically robust research investigating APP patient satisfaction and experience.

Types of adventitious sounds and its characteristics.

The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.

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

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.
All hazard areas shown on the hazard map are included.Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data.These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards.

Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones.However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area.The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure).

Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards.

Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Archaeologists generate large numbers of digital materials during the course of field, laboratory, and records investigations. Maps, photographs, data analysis, and reports are often produced digitally. Good curation of digital data means it can be discovered and accessed, and preserving these materials means they are accessible for future use. In many ways the managing, curating and preserving digital materials involves similar steps as those taken with physical artifacts, samples, and paper records. However, the digital materials are different and the process can appear daunting at first.

In this poster we outline some simple steps for managing and curating digital materials that can be integrated into existing or future project and that can be applied to digital materials from completed projects. We will also use real world examples from tDAR (the Digital Archaeological Record) to illustrate how people are preserving their digital materials for access and future use.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area.The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included.Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data.These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”.These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones.However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class). The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard.The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area.The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data.These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”.These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class). Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Sound and analysis type.

Oceanic Upper Layer Nutrient Climatologies (nitrate, phosphate, silicate) on 2° grid

Description copied from: SMP site

OpenDAP access: data access via OpenDAP

Introduction The phosphate, nitrate and silicate data used to produce the monthly climatologies were taken from the World Ocean Atlas 1998 (Conkright et al., 1998), produced by the Ocean Climate Laboratory at the National Oceanographic Data Center (NODC). The number of available profiles varies from one parameter to another: 318,800 for phosphate, 200,651 for silicate and 73,471 for nitrate. This means that the accuracy of the monthly maps will be different from one nutrient to another. The procedure of producing monthly climatologies of nutrients is very similar to that used by Najjar and Keeling (1997) to produce a monthly climatology of the oxygen anomaly. The main difference is that nutrient-temperature relationships are used to filter and extrapolate the nutrient data.

Relationship between nutrients and temperature Good relationships between nutrients and temperature were found in the surface ocean by Kamykowski and Zentara (1985, 1986), Takahashi et al. (1993), and many others. We explored nutrient-temperature relationships for the NODC data sets for the upper 500 m of the ocean to see if they could be used for filtering and extrapolation of the nutrient data. We found that nutrient-temperature relationships varied spatially, so we partitioned the ocean into eight regions. For each region, we defined parabolic least-squares fits for temperature below 25°C, and linear fits for temperature between 25° and 30°C. We assumed that for temperature greater than 30°C, nutrient concentrations were zero. The eight regions used were: North Pacific (15N - 60N), North Atlantic including Arctic ocean (north of 15N), Tropical Pacific, Tropical Atlantic, (15N - 15S for both of them), Tropical Indian, (North of 15S), South Pacific, South Atlantic and South Indian (south of 15S). We found that segregation of the data as a function of time and depth did not noticeably improve the regressions. r² values varied from 0.50 to 0.85. The best fits were obtained for the high Southern latitudes and the North Pacific.

Filtering the data We took advantage of the extensive quality control procedures performed by Conkright et al. (1998) and used only those data that passed all of their tests. This left us with 287,554 phosphate profiles, 171,141 silicate profiles and 66446 nitrate profiles. We still found, however, that many outliers remained in the data, so we conducted additional filtering using the nutrient-temperature relationships described above. We simply deleted all nutrient data that were more than a defined deviation from the least squares fit for each region. The deviations were a function of temperature; as we didn't want to smooth the seasonal signal of the nutrients, we took larger intervals of tolerance (two to three standard deviationa) for cold waters (where the seasonal signal is strong) and moderate intervals (about one standard deviation) for warm waters.

Vertical interpolation We vertically interpolated the data to the top 14 NODC standard levels (0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 m) using the monotonic scheme of Steffen (1990), as described in Najjar and Keeling (1997). In some cases interpolation could not be performed because of poor vertical resolution of the measurements. The final number of profiles of nutrients used for the mapping were:

PO4: 183,451 profiles (discarded 42% of the initial profiles)
NO3: 48,254 profiles (discarded 34%)
SiO2: 121,124 profiles (discarded 40%)

Most of the discarded profiles (approx. 2/3 of the profiles used) failed in the vertical interpolation processing. That means that the profiles discarded contain 2 or less data. The other profiles were presumably not representative of open ocean conditions (near-shore, coastal profiles ...).

Binning the data on the equal-area grid The equal-area grid described in Najjar and Keeling (1997) was used for the horizontal spacing of the nutrient maps. The grid is 2 degrees in latitude and variable in... Visit https://dataone.org/datasets/sha256%3Afb0b0f90cb2e09cc64ce4f84f1c785812c046a2acf2ae8de34d66cab04097b09 for complete metadata about this dataset.

Oceanic Upper Layer Nutrient Climatologies (nitrate, phosphate, silicate) on 2° grid

Description copied from: SMP site

OpenDAP access: data access via OpenDAP

Introduction The phosphate, nitrate and silicate data used to produce the monthly climatologies were taken from the World Ocean Atlas 1998 (Conkright et al., 1998), produced by the Ocean Climate Laboratory at the National Oceanographic Data Center (NODC). The number of available profiles varies from one parameter to another: 318,800 for phosphate, 200,651 for silicate and 73,471 for nitrate. This means that the accuracy of the monthly maps will be different from one nutrient to another. The procedure of producing monthly climatologies of nutrients is very similar to that used by Najjar and Keeling (1997) to produce a monthly climatology of the oxygen anomaly. The main difference is that nutrient-temperature relationships are used to filter and extrapolate the nutrient data.

Relationship between nutrients and temperature Good relationships between nutrients and temperature were found in the surface ocean by Kamykowski and Zentara (1985, 1986), Takahashi et al. (1993), and many others. We explored nutrient-temperature relationships for the NODC data sets for the upper 500 m of the ocean to see if they could be used for filtering and extrapolation of the nutrient data. We found that nutrient-temperature relationships varied spatially, so we partitioned the ocean into eight regions. For each region, we defined parabolic least-squares fits for temperature below 25°C, and linear fits for temperature between 25° and 30°C. We assumed that for temperature greater than 30°C, nutrient concentrations were zero. The eight regions used were: North Pacific (15N - 60N), North Atlantic including Arctic ocean (north of 15N), Tropical Pacific, Tropical Atlantic, (15N - 15S for both of them), Tropical Indian, (North of 15S), South Pacific, South Atlantic and South Indian (south of 15S). We found that segregation of the data as a function of time and depth did not noticeably improve the regressions. r² values varied from 0.50 to 0.85. The best fits were obtained for the high Southern latitudes and the North Pacific.

Filtering the data We took advantage of the extensive quality control procedures performed by Conkright et al. (1998) and used only those data that passed all of their tests. This left us with 287,554 phosphate profiles, 171,141 silicate profiles and 66446 nitrate profiles. We still found, however, that many outliers remained in the data, so we conducted additional filtering using the nutrient-temperature relationships described above. We simply deleted all nutrient data that were more than a defined deviation from the least squares fit for each region. The deviations were a function of temperature; as we didn't want to smooth the seasonal signal of the nutrients, we took larger intervals of tolerance (two to three standard deviationa) for cold waters (where the seasonal signal is strong) and moderate intervals (about one standard deviation) for warm waters.

Vertical interpolation We vertically interpolated the data to the top 14 NODC standard levels (0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400 and 500 m) using the monotonic scheme of Steffen (1990), as described in Najjar and Keeling (1997). In some cases interpolation could not be performed because of poor vertical resolution of the measurements. The final number of profiles of nutrients used for the mapping were:

PO4: 183,451 profiles (discarded 42% of the initial profiles)
NO3: 48,254 profiles (discarded 34%)
SiO2: 121,124 profiles (discarded 40%)

Most of the discarded profiles (approx. 2/3 of the profiles used) failed in the vertical interpolation processing. That means that the profiles discarded contain 2 or less data. The other profiles were presumably not representative of open ocean conditions (near-shore, coastal profiles ...).

Binning the data on the equal-area grid The equal-area grid described in Najjar and Keeling (1997) was used for the horizontal spacing of the nutrient maps. The grid is 2 degrees in latitude and variable in... Visit https://dataone.org/datasets/sha256%3Ae5a4274901917f226a1ab0bd86a40a8089784f36ea16112e751f166453c8df39 for complete metadata about this dataset.

Definitions of codes used in the qualitative data synthesis.

The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.

PurposeAlthough subjective knowledge about the prognosis of an advanced disease is extremely important for coping and treatment planning, the concept of prognostic awareness (PA) remains inconsistently defined. The aims of the scoping review were to synthesize a definition of PA from the most recent literature, describe preconditions, correlates and consequences, and suggest a conceptual model.MethodsBy using scoping review methodology, we searched the Web of Science and PubMed databases, and included publications, reviews, meta-analyses or guidelines on all physical diagnoses, as well as publications offering a conceptual or an operational definition of PA. The data were analyzed by means of content analysis techniques.ResultsOf the 24 included publications, 21 referred exclusively to cancer, one to patients with hip fractures and two to palliative care in general. The deduced definition of PA comprised the following facets: adequate estimation of chances for recovery, knowledge of limited time to live, adequate estimation of life expectancy, knowledge of therapy goals, and knowledge of the course of the disease. Further content analysis results were mapped graphically and in a detailed table.ConclusionThere appears to be a lack of theoretical embedding of PA that in turn influences the methods used for empirical investigation. Drawing on a clear conceptual definition, longitudinal or experimental studies would be desirable.