This dataset was developed to understand the nutrient content of the commonly consumed foods in New Zealand.

• The linkages between two prominent New Zealand studies (or databases) were evaluated, in order to determine the matching between the foods in those studies.
• The first study was the 2008/09 New Zealand Adult Nutrition Survey (NZANS) for the total New Zealand population, which presents the commonly consumed foods (a total of 346) in New Zealand.
• The second study was the New Zealand FOODfiles™ 2018 Version 01, which is a database that provides nutrition information (both essential nutrients and essential amino acids) for 2,767 foods.
• Some of the foods in the 2008/09 NZANS are dishes (or menus); the nutrient content of each of those dishes was constructed based on the work by Drew et al. (2020).

References

1. Drew J, Cleghorn C, Macmillan A, Mizdrak A. 2020. Healthy and Climate-Friendly Eating Patterns in the New Zealand Context. Environ Health Perspect. 128(1): 017007.
2. Plant and Food Research, Ministry of Health. 2019. New Zealand Food Composition Database. The New Zealand Institute for Plant and Food Research Limited. [accessed 12 Feb 2020]. https://www.foodcomposition.co.nz/
3. University of Otago, Ministry of Health. 2011. A Focus on Nutrition: Key Findings of the 2008/09 New Zealand Adult Nutrition Survey. Wellington, New Zealand: Ministry of Health.

Last update: 12 September 2020

MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.

MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.

Land Environments of New Zealand (LENZ) is a classification of fifteen climate, landform, and soil variables chosen for their relevance to biological distributions. Classification groups were derived by automatic classification using a multivariate procedure. Four levels of classification detail have been produced from this analysis, containing 20, 100, 200, and 500 groups respectively. More information is available from the LENZ web site: http://www.landcareresearch.co.nz/databases/lenz/

Land Environments of New Zealand (LENZ) is a classification of fifteen climate, landform, and soil variables chosen for their relevance to biological distributions. Classification groups were derived by automatic classification using a multivariate procedure. Four levels of classification detail have been produced from this analysis, containing 20, 100, 200, and 500 groups respectively. More information is available from the LENZ web site: http://www.landcareresearch.co.nz/databases/lenz/

Summary of key microbial water quality databasesa.

This dataset contains major outputs of a study on collaboration links between all New Zealand universities based on Scopus publications within 2010-2015. For more details about the study, one may refer toSamin Aref, David Friggens, and Shaun Hendy. 2018. Analysing Scientific Collaborations of New Zealand Institutions using Scopus Bibliometric Data. In Proceedings of ACSW 2018: Australasian Computer Science Week 2018, January 29-February 2, 2018, Brisbane, QLD, Australia, 10 pages.

https://doi.org/10.1145/3167918.3167920The number of collaboration records of 15 New Zealand universities and crown research institutes (government-funded research centres) are categorised based on the type of collaborator (business enterprise, government, higher education, and private not-for-profit) and the subject of publication venue and provided as a CSV file.New Zealand scientific collaboration network is provided in 3 formats: .gephi, .graphml, and an edge list in .csv format7 Induced subgraphs of the network are also provided. Each induced subgraph (ego network) represents the collaborators network of a crown research institute. Two formats are provided: .gephi and .graphml.Please cite the research paper (doi: 10.1145/3167918.3167920) as well as this Figshare dataset (doi 10.6084/m9.figshare.5705167) when using the data.ACKNOWLEDGMENTSAndrew Marriott and Sam Holmes at Ministry of Business Innovation & Employment (MBIE) performed much of the work classifying New Zealand institutions. The first author would like to thank Peter Ellis and Franz Smith for their support.

Analytical tools and databases that use predictive microbiology to support safe water and/or safe sanitation systems.

The traits of organisms provide critical information for understanding changes in biodiversity and ecosystem function at large scales. In recent years, trait databases of macroinvertebrates have been developed across continents. Anyone using different databases to search for traits will encounter a series of problems that lead to uncertain results due to the inconsistency of the trait information. For example, traits for a particular macroinvertebrate taxon may be inconsistent across databases, coded in inconsistent ways, or cannot be found. However, most of the current studies do not clearly state their solutions, which seriously hinders the accuracy and comparability of global trait studies. To solve these problems, we collected representative databases from several continents, including the United States, Europe, South Africa, Bolivia, Australia, and New Zealand. By comparing the inconsistency of similar trait classifications in the nine databases, we harmonized 41 of these grouping features. We found that these databases differed widely in terms of the range and category of traits. And the method of coding traits also varies from database to database. Moreover, we showed a set of trait searching rules that integrate trait databases from different regions of the world, allowing traits to be identified more easily and uniformly using different trait databases worldwide. We also applied this method to determine the traits of 155 macroinvertebrate taxa in the Three Parallel Rivers Region (TPRR). The results showed that among a total of 155 macroinvertebrate taxa, the 41 grouping features of all genera were not fully identified, and 32 genera were not recorded (thus using family-level data). No trait information was found at all for two families, which contain two genera. This suggests that many macroinvertebrate taxa and their traits have not been fully studied, especially in those regions, including China, where macroinvertebrate trait studies are lagging. This inadequacy and unevenness have seriously hindered the study and development of macroinvertebrate trait and functional diversity worldwide. Our results complement the information on stream macroinvertebrate traits in the TPRR, a global biodiversity hotspot, and greatly promote the uniformity of global trait research and the accuracy and comparability of trait research in different regions.

a Food Standards Australia and New Zealand,b United States Department of Agriculture,c Food Standards Agency,d Separate databases for flavonoids, carotenoids, proanthocyanidins and isoflavones,e Eurofir EBASIS contains bioactive data for UK and Europe,f National Health Survey,ghttps://www.xyris.com.au/foodworks/fw_pro.html,hhttp://www.nutribase.com/highend.html,ihttp://www.foodresearch.ca/wp-content/uploads/2013/06/candat-features-1.pdf,j Tinuviel Software,i Downlees Systems,k Forestfield Software,l Kelicomp,mhttp://www.tinuvielsoftware.com/faqs.htm,nhttp://www.dietsoftware.com/canada.html,o Text file: a file that only contains text,p A file containing tables of information stored in columns and separated by tabs (can be exported into almost any spreadsheet program),q Microsoft Excel spreadsheet,r Microsoft Access Database file: is a database file with automated functions and queries,s American Standard Code for Information Interchange (a standard file type that can be used by many programs),t Database File Format (this file type can be opened with Microsoft Excel and Access),u information to create Excel or PDF available,v Composition of Foods, Australia,w International Network of Food Data System,x Users guide states food name is most descriptive & recognisable of food referencedyhttp://www.foodstandards.gov.au/science/monitoringnutrients/nutrientables/nuttab/Pages/NUTTAB-2010-electronic-database-files.aspx,zhttp://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/default.aspx,aahttp://ndb.nal.usda.gov/ndb/search/list,bbhttp://tna.europarchive.org/20110116113217/http://www.food.gov.uk/science/dietarysurveys/dietsurveys/,cchttp://webprod3.hc-sc.gc.ca/cnf-fce/index-eng.jspDesktop analysis and examination of six key food composition databases format.

BackgroundSelf-harm and suicide behaviours are a major public health concern. Several factors are associated with these behaviours among military communities. Identifying these factors may have important implications for policy and clinical services. The aim of this review was to identify the risk and protective factors associated with self-harm and suicide behaviours among serving and ex-serving personnel of the United Kingdom Armed Forces, Canadian Armed Forces, Australian Defence Force and New Zealand Defence Force.MethodsA systematic search of seven online databases (PubMed, Web of Science, Embase, Global Health, PsycINFO, PTSDpubs and CINAHL) was conducted alongside cross-referencing, in October 2022. Following an a priori PROSPERO approved protocol (CRD42022348867), papers were independently screened and assessed for quality. Data were synthesised using a narrative approach.ResultsOverall, 28 papers were included: 13 from Canada, 10 from the United Kingdom, five from Australia and none from New Zealand. Identified risk factors included being single/ex-relationship, early service leavers, shorter length of service (but not necessarily early service leavers), junior ranks, exposure to deployment-related traumatic events, physical and mental health diagnoses, and experience of childhood adversity. Protective factors included being married/in a relationship, higher educational attainment, employment, senior ranks, and higher levels of perceived social support.ConclusionAdequate care and support are a necessity for the military community. Prevention and intervention strategies for self-harm and suicide behaviours may be introduced early and may promote social networks as a key source of support. This review found a paucity of peer-reviewed research within some populations. More peer-reviewed research is needed, particularly among these populations where current work is limited, and regarding modifiable risk and protective factors.

Initiatives for women, newborns and families with risk of adverse outcomes by country.

Risk and Protective Factors Identified in Included Studies (N = 28).