The Netherlands Biodiversity Data Services and the R package nbaR: Automated workflows for biodiversity data analysis

The value of data present in natural history collections for research in biodiversity, ecology and evolution cannot be overstated. Naturalis Biodiversity Center of the Netherlands, home to one of the largest natural history collections in the world, launched a large-scale digitisation project resulting in the registration of more than 38 million specimen objects, many of them annotated with descriptive metadata, such as geographic coordinates or multimedia content. Other resources hosted at Naturalis include species occurrence records and comprehensive taxonomic checklists, such as the Catalogue of Life. As our institution strongly believes in the Open Science paradigm, we seek to make our data available to the global biodiversity research community, enhancing data analysis workflows, as for example (i) the modelling of present, past and future species distributions using specimen occurrence data, (ii) time calibration of (molecular) phylogenies using dated specimen occurrences, (iii) taxonomic name resolution or (iv) image data mining. To this end, we developed the Netherlands Biodiversity Data services [1], providing centralized access to biodiversity data via state of the art, open access interfaces and a mechanism to assign persistent identifiers to all records. Data are retrieved from heterogeneous sources and harmonized into a document store that complies with international data standards such as ABCD (Access to Biological Collection Data [2]). Employing the Elasticsearch engine, our infrastructure features complex query options, near real-time queries, and scaling possibilities to secure foreseen data growth. Focusing on availability and accessibility, the services were designed as a versatile, low-level REST API to allow the use of our data in a broad variety of applications and services. For programmatic access to our data services, we developed client libraries for several programming languages. Here we present the R package ‘nbaR’ [3], a client especially targeted to an audience of biodiversity researchers. The R programming language has found wide acceptance in this field over the past years and our package facilitates convenient means to connect our data resources to existing tools for statistical modelling and analysis. The abstraction layer introduced by the client lets the user formulate even complex queries in a convenient manner, thereby lowering the access threshold to our data services. We will demonstrate the potential and benefits of services and R client by integrating nbaR with state-of-the art packages for species distribution modelling and time calibration of phylogenetic trees into a single analysis workflow. 1. Netherlands Biodiversity Data services – User documentation. http://docs.biodiversitydata.nl (accessed 17 May 2018). 2. Access to Biological Collections Data task group. 2007. Access to Biological Collection Data (ABCD), Version 2.06. Biodiversity Information Standards (TDWG) http://www.tdwg.org/standards/115 (accessed 17 May 2018). 3. nbaR GitHub repository. https://github.com/naturalis/ nbaR (accessed 17 May 2018)

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma

BACKGROUND: Digital inhalers can monitor inhaler usage, support difficult-to-treat asthma management and inform step-up treatment decisions yet their economic value is unknown, hampering wide-scale implementation.OBJECTIVE: We aimed to assess the long-term cost-effectiveness of digital inhaler-based medication adherence management in difficult-to-treat asthma.METHODS: A model-based cost-utility analysis was performed. The Markov model structure was determined by biological and clinical understanding of asthma and was further informed by guideline-based assessment of model development. Internal and external validation was performed using the AdViSHE tool. The INCA Sun randomized clinical trial data were incorporated into the model to evaluate the cost-effectiveness of digital inhalers. Several long-term clinical case scenarios were assessed (reduced number of exacerbations, increased asthma control, introduction of biosimilars [25% price-cut on biologics]).RESULTS: The long-term modelled cost-effectiveness based on a societal perspective indicated 1-year per-patient costs for digital inhalers and usual care (i.e., regular inhalers) of €7,546 and €10,752, respectively, reflecting cost savings of €3,207 for digital inhalers. Using a 10-year intervention duration and time horizon resulted incost savings of €26,309 for digital inhalers. In the first year, add-on biologic therapies accounted for 69% of the total costs in the usual care group, and for 49% in the digital inhaler group. Scenario analyses indicated consistent cost savings ranging from €2,287 (introduction biosimilars) to €4,581 (increased control, decreased exacerbations).CONCLUSION: In patients with difficult-to-treat asthma, digital inhaler based interventions can be cost-saving on the long-term by optimizing medication adherence and inhaler technique and reducing add-on biologic prescriptions.</p

Leveraging the Benefits of Open Data Services for Natural History Collection Management

The value of data present in natural history collections for research and collection management cannot be overstated. Naturalis Biodiversity Center, home to one of the largest natural history collections in the world, completed a large-scale digitisation project resulting in the registration of more than 38 million objects, many of them annotated with descriptive metadata, such as geographic coordinates and multimedia content. While digitisation is ongoing, we are now also looking for ways to leverage our digital collection, both for the benefit of collection management and that of networking with other natural history collections. To this end, we developed the Netherlands Biodiversity Data Services, providing centralized access to our collection data via state of the art, open access interfaces. Full, centralized access to the digital collection allows us to combine the data with other sources, such as collection scans focusing on the physical condition and accessibility of the collection. But also with data from external sources, such as the collection information of sister institutions, allowing for combining and comparing data, and exploring areas where collections can reinforce each other. Focusing on availability and accessibility, the services were deliberately designed as a versatile, low-level API to allow the use of our data with a broad variety of applications and services. These applications range from scientific research and remote mobile access to collection information, to "mash ups" with other data sources, apps and application in our own museum. We will demonstrate this range of applications through several examples, including the embedding of data in websites (example, Dutch Caribbean Species Register: http://www.dutchcaribbeanspecies.org/linnaeus_ng/app/views/species/nsr_taxon.php?id=177968&cat=165), use in the development of deep learning models, thematic portals (example, Naturalis meteorite collection: http://bioportal.naturalis.nl/result?theme=meteorites&language=en) and the development of Java- and R-clients. This presentation ties in with Max Caspers' presentation "Advancing collections management with the Netherlands Biodiversity Data Services", in which he will demonstratie the potential of the services described in this presentation for the area of collections management, specifically

Advancing Collections Management with the Netherlands Biodiversity Data Services

By the summer of 2015 Naturalis Biodiversity Center had come to the end of a five-year digitization programme that aimed at digitally disclosing the entire collection of, at the time, 38 million objects. The result was a vast amount of collections data being made available to researchers, collection managers and the public. In order to utilize these data to their full extent, Naturalis has in the past few years been developing the Netherlands Biodiversity Data Services (NBDS). These services "speak" not only to our digitized collection, but to other sources of information as well and lets us query and use these data in a centralized manner. While the NBDS open up a lot of possibilities for i.e. communication, exhibition, education, policy making, etc., a very important field for its application is collection management. Instead of managing (at this point) 41 million individual objects, the NBDS could provide insight into custom aggregations of data to further professionalize decision making. Not only detailed information about taxonomy, gathering events and collection history can be provided, one can also think about quantifying use, conservation status, change in collection-size over time, etc. Some examples of application for collections management will be given during the presentation and illustrated with a collections dashboard. Even though we have made great progress in digitization, certain parts of our collection are not digitized to specimen-level and to various degrees of completeness, parts of the physical collection are not identified to species level, not all data are consistent or properly validated, etc. But instead of this limiting the applicability of the NBDS, the data service can be used as a tool to pinpoint these areas for improvement and to allow collection management to properly address and prioritize them. This presentation ultimately deals with the potential the NBDSNBA has for managing collections, both physical as digital, and enhancing their quality and value

Supporting citizen scientists with automatic species identification using deep learning image recognition models

Volunteers, researchers and citizen scientists are important contributors to observation and monitoring databases. Their contributions thus become part of a global digital data pool, that forms the basis for important and powerful tools for conservation, research, education and policy. With the data contributed by citizen scientists also come concerns about data completeness and quality. For data generated by citizen scientists taxonomic bias effects, where certain species (groups) are underrepresented in observations, are even stronger than for professionally collected data. Identification tools that help citizen scientists to access more difficult, underrepresented groups, can help to close this gap. We are exploring the possibilities of using artificial intelligence for automatic species identification as a tool to support the registration of field observations. Our aim is to offer nature enthusiasts the possibility of automatically identifying species, based on photos they have taken as part of an observation. Furthermore, by allowing them to register these identifications as part of the observation, we aim to enhance the completeness and quality of the observation database. We will demonstrate the use of automatic species recognition as part of the process of observation registration, using a recognition model that is based on deep learning techniques. We investigated the automatic species recognition using deep learning models trained with observation data of the popular website Observation.org (https://observation.org/). At Observation.org data quality is ensured by a review process of all observations by experts. Using the pictures and corresponding validated metadata from their database, models were developed covering several species groups. These techniques were based on earlier work that culminated in ObsIdentify, an free offline mobile app for identifying species based on pictures taken in the field. The models are also made available as an API web service, which allows for identification by offering a photo through common HTTP-communication - essentially like uploading it through a webpage. This web service was implemented in the observation entry workflows of Observation.org. By providing an automatically generated taxonomic identification with each image, we expect to stimulate existing citizen scientists to generate a larger quantity of and more biodiverse observations. Additionally we hope to motivate new citizen scientists to start contributing. Additionally, we investigated the use of image recognition for the identification of additional species in the photo other than the primary subject, for example the identification of the host plant in photos of insects. The Observation.org database contains many of such photos which are associated with a single species observation, while additional, other species are also present in the photo, but are unidentified. Combining object detection to detect individual species with species recognition models opens up the possibility of automatically identifying and counting these species, enhancing the quality of the observations. In the presentation we will present the initial results of this application of deep learning technology, and discuss the possibilities and challenges