bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transforming Biodiversity Data into Knowledge for Decision-making
Authors: Weatherdon Lauren 
Published: June 2019

Ensuring that we have the data and information necessary to make informed decisions is a core requirement in an era of increasing complexity and anthropogenic impact. With cumulative challenges such as the decline in biodiversity and accelerating climate change, the need for spatially-explicit and methodologically-consistent data that can be compiled to produce useful and reliable indicators of biological change and ecosystem health is growing.
Technological advances—including satellite imagery—are beginning to make this a reality, yet uptake of biodiversity information standards and scaling of data to ensure its applicability at multiple levels of decision-making are still in progress. The complementary Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs), combined with Darwin Core and other data and metadata standards, provide the underpinnings necessary to produce data that can inform indicators. However, perhaps the largest challenge in developing global, biological change indicators is achieving consistent and holistic coverage over time, with recognition of biodiversity data as global assets that are critical to tracking progress toward the UN Sustainable Development Goals and Targets set by the international community (see Jensen and Campbell (2019) for discussion).
Through this talk, I will describe some of the efforts towards producing and collating effective biodiversity indicators, such as those based on authoritative datasets like the World Database on Protected Areas (https://www.protectedplanet.net/), and work achieved through the Biodiversity Indicators Partnership (https://www.bipindicators.net/). I will also highlight some of the characteristics of effective indicators, and global biodiversity reporting and communication needs as we approach 2020 and beyond.
Towards Linked Open Molecular Data: Recommendations for researchers, collections, infrastructures and publishers
Authors: Droege Gabriele , Karsch-Mizrachi Ilene , Barker Katharine , Coddington Jonathan , Seberg Ole 
Published: June 2019

The variety of molecular methods used to analyze biosamples is continuously increasing, as is the need for the standardized deposition, documentation and citation of both the samples as well as the methods applied to them. Global initiatives such as the International Nucleotide Sequence Database Collaboration (INSDC, http://www.insdc.org), Barcode of Life Data System (BOLD, http://www.boldsystems.org), the Global Biodiversity Information Facility (GBIF, http://www.gbif.org) and the Global Genome Biodiversity Network (GGBN, http://www.ggbn.org), in addition to many others, have been working towards standardized access to biological data for many years. Collectively, these biodiversity data management platforms provide a considerable and indispensable infrastructure to the research community. However, cross-linking the massive amounts of protein and DNA sequence data submitted to these databases every year with standardized records of the underlying biological material remains challenging. Best practices for standardized data submissions and data citations are urgently needed.
In the long run, two goals should be achieved above all else:

all sequence data should be linked to natural history collections, and

biological material that was used for molecular research, especially DNA sequencing, should be deposited and, thus, made accessible in public, well curated collections.

all sequence data should be linked to natural history collections, and
biological material that was used for molecular research, especially DNA sequencing, should be deposited and, thus, made accessible in public, well curated collections.
Here we will provide recommendations both for researchers and collections how to cite underlying biological material at INSDC and in publications in a standardized way towards Linked Open Data. We will also address how the global infrastructures and publishers can improve their interoperability.
Using Named Graphs and Knowledge Graph Template Patterns for Efficiently Organizing FAIR Anatomy Data and Metadata
Authors: Vogt Lars , Baum Roman 
Published: June 2019

Currently, morphological data and metadata are still mostly published as unstructured free texts, which lack semantic transparency, cannot be parsed by computers, and do not comply with the FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. (2016) data principles, thus hampering their reuse by non-experts and their integration across many fields in the life sciences. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available.
Instead of free text descriptions, morphological data and metadata can be recorded, stored, and communicated through the Web in the form of Resource Description Framework (RDF) triple statements that use the ‘Subject – Property – Object’ syntax of RDF and URIs of ontology classes and properties as well as URIs for individual entities as terminology. Since a given URI can take the ‘Subject’ position in one and the ‘Object’ position in another RDF statement, several triples can be linked to form a highly formalized and structured directed graph (semantic graph).
After introducing an instance-based approach of recording morphological descriptions and their accompanying metadata as semantic knowledge graphs (i.e. Anatomy Knowledge Graphs), we propose a knowledge graph template pattern for each type of anatomical observation and a pattern for documenting metadata. The use of template patterns for knowledge graphs provides Interoperability and Reusability of comparable anatomical observations and of their accompanying metadata and a means to meaningfully visualize information contained in semantic graphs in a user-friendly HTML representation. Stored in a tuple store, Anatomy Knowledge Graphs become Findable and Accessible through the store’s SPARQL endpoint. As a consequence, anatomy data and metadata documented as Anatomy Knowledge Graphs in a tuple store are FAIR.
Finally, we suggest a general scheme of how to efficiently organize Anatomy Knowledge Graphs in a tuple store framework based on instances of named graphs, with each individual named graph instantiating an ontology class that relates to a particular type of observation (e.g., weight measurement named graph class). A named graph is a fourth element in an RDF statement (‘Subject – Property – Object – Named Graph’), turning the triple into a quadruple. All RDF statements that share the same URI in the ‘Named Graph’ position belong to the same named graph. The use of named graph resources allows meaningful fragmentation of the contents of an Anatomy Knowledge Graph (Fig. 1), which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data and metadata. This scheme has been implemented in the description module of the prototype for semantic Morph∙D∙Base.
When Data Management Meets Project Management
Authors: Meyke Evgeniy 
Published: June 2019

Complex projects that collect, curate and analyse biodiversity data are often presented with the challenge of accommodating diverse data types, various curation and output workflows, and evolving project logistics that require rapid changes in the applications and data structures. At the same time, sustainability concerns and maintenance overheads pose a risk to the long term viability of such projects.
We advocate the use of flexible, multiplatform tools that adapt to operational, day-to-day challenges while providing a robust, cost efficient, and maintainable framework that serves the needs data collectors, managers and users.
EarthCape is a highly versatile platform for managing biodiversity research and collections data, associated molecular laboratory data (Fig. 1), multimedia, structured ecological surveys and monitoring schemes, and more. The platform includes a fully functional Windows client as well as a web application. The data are stored in the cloud or on-premises and can be accessed by users with various access and editing rights. Ease of customization (making changes to user interface and functionality) is critical for most environments that deal with operational research processes. For active researchers and curators, there is rarely time to wait for a cycle of development that follows a change or feature request. In EarthCape, most of the changes to the default setup can be implemented by the end users with minimum effort and require no programming skills.
High flexibility and a range of customisation options is complemented with mapping to Darwin Core standard and integration with GBIF, Geolocate, Genbank, and Biodiversity Heritage Library APIs. The system is currently used daily for rapid data entry, digitization and sample tracking, by such organisations as Imperial College, University of Cambridge, University of Helsinki, University of Oxford.
Being an operational data entry and retrieval tool, EarthCape sits at the bottom of Virtual Research Environments ecosystem. It is not a software or platform to build data repositories, but rather a very focused tool falling under "back office" software category. Routine label printing, laboratory notebook maintenance, rapid data entry set up, or any other of relatively loaded user interfaces make use of any industry standard relational database back end. This opens a wide scope for IT designers to implement desired integrations within their institutional infrastructure. APIs and developer access to core EarthCape libraries to build own applications and modules are under development.
Basic data visualisation (charts, pivots, dashboards), mapping (full featured desktop GIS module), data outputs (report and label designer) are tailored not only to research analyses, but also for managing logistics and communication when working on (data) papers. The presentation will focus on the software platform featuring most prominent use cases from two areas: ecological research (managing complex network data digitization project) and museum collections management (herbarium and insect collections).
FinBIF: An all-embracing, integrated, cross-sectoral biodiversity data infrastructure
Authors: Schulman Leif , Juslén Aino , Lahti Kari 
Published: June 2019

The service model of the Global Biodiversity Information Facility (GBIF) is being implemented in an increasing number of national biodiversity (BD) data services. While GBIF already shares &gt;109 data points, national initiatives are an essential component: increase in GBIF-mediated data relies on national data mobilisation and GBIF is not optimised to support local use. The Finnish Biodiversity Information Facility (FinBIF), initiated in 2012 and operational since late 2016, is one of the more recent examples of national BD research infrastructures (RIs) – and arguably among the most comprehensive. Here, we describe FinBIF’s development and service integration, and provide a model approach for the construction of all-inclusive national BD RIs.
FinBIF integrates a wide array of BD RI approaches under the same umbrella. These include large-scale and multi-technology digitisation of natural history collections; building a national DNA barcode reference library and linking it to species occurrence data; citizen science platforms enabling recording, managing and sharing of observation data; management and sharing of restricted data among authorities; community-driven species identification support; an e-learning environment for species identification; and IUCN Red Listing (Fig. 1).
FinBIF’s aims are to accelerate digitisation, mobilisation, and distribution of biodiversity data and to boost their use in research and education, environmental administration, and the private sector. The core functionalities of FinBIF were built in a 3.5-year project (01/2015–06/2018) by a consortium of four university-based natural history collection facilities led by the Finnish Museum of Natural History Luomus. Close to 30% of the total funding was granted through the Finnish Research Infrastructures programme (FIRI) governed by the national research council and based on scientific excellence. Government funds for productivity enhancement in state administration covered c.40 % of the development and the rest was self-financed by the implementing consortium of organisations that have both a research and an education mission.
The cross-sectoral scope of FinBIF has led to rapid uptake and a broad user base of its functionalities and services. Not only researchers but also administrative authorities, various enterprises and a large number of private citizens show a significant interest in the RI (Table 1).
FinBIF is now in its second construction cycle (2019–2022), funded through the FIRI programme and, thus, focused on researcher services. The work programme includes integration of tools for data management in ecological restoration and e-Lab tools for spatial analyses, morphometric analysis of 3D images, species identification from sound recordings, and metagenomics analyses.
Using Unsupervised Artificial Neural Networks to Detect Sibling Species: A case in Myxomycetes
Authors: Pando Francisco , Heredia Ignacio , Lloret Lara 
Published: June 2019

Species distribution modelling (SDM) --i.e. the prediction of species potential geographic distributions based on correlations between known presence records and the environmental conditions at occurrence localities-- is one of the most freqently cited developments in recent years in the realm of biodiversity studies (Web of Science 2019). The reasons for the explosion in SDM studies reside in: a) the pressure to know species distributions (under both present and future climate change scenarios) with precision to satisfy scientific as well as societal objectives; b) the impossibility of knowing every occurrence of all but the most conspicuous and/or special interest species; c) the availability of primary occurrence and environmental data in unprecedented amounts, thanks to initiatives like the Global Biodiversity Information Facility (GBIF); and d) the development of algorithms and software, along with the computing power, which allow inference of species distribution models from the available data.
The standard methods to produce such models are based on environmental feature vectors, and some well-established algorithms such as distance-based machine learning, regression or a combination of these (Tsoar et al. 2007).
In this presentation, we explore deep learning techniques (LeCun et al. 2015), particularly how those developed in recent years could contribute to the study of species distribution (see also Botella et al. 2018; Deneu et al. 2018). In this contribution we aim to identify sibling species on the basis of their ecological preferences. In this exercise, we prepare an image-based environmental representation space using an unsupervised classification approach, instead of a set of environmental feature vectors. For our case study, we chose a well-known cosmopolitan myxomycete (i.e., mycetozoan) species: Hemitrichia serpula (Scop.) Rostaf., whose ecological preferences --involving several biomes-- may suggest that it could comprise several sibling species.
Different Approaches between First and Second Mass Digitisation Project for the Herbarium (BR) at Meise Botanic Garden
Authors: De Smedt Sofie , Bogaerts Ann , Engledow Henry , Groom Quentin 
Published: June 2019

The Herbarium of Meise Botanic Garden is in the top 15 herbaria worldwide. The collection comprises some four million specimens, which are important for scientific research.
Digitisation of specimens includes imaging, transcription of label information, linking data and making the results publicly accessible online. In addition to facilitating researchers’ access to specimens, digitisation also brings new possibilities for analysis and discovery of new data, such as the vast amount of information on handwritten labels.
In the DOE! project (Digitale Ontsluiting Erfgoedcollecties), funded by the Flemish Government, 1.2 million herbarium sheets from the African and Belgian collections were digitised. We have received additional funding to digitise a further 1.4 million specimens for the remaining vascular plants and macro-algae collections, by October 2021. These include the historic collections of Von Martius and Van Heurck.
Carl Friedrich Philipp von Martius (1794–1868) was a pioneering explorer whose expeditions led to the discovery of many species. He amassed over 300,000 specimens, some of which were used to compile the first Flora of Brazil.
Henri Van Heurck (1838–1909) also gathered herbarium specimens from all over the world, including a specimen originally from the collection of Linnaeus.
Despite this being our second mass digitisation project, there are significant differences in our approach. This is partly due to lessons learned from the first project and partly to the nature of the collections themselves. The differences in the tendering process, specimen preparation, workflow and data capture will be explained.
Making these specimens openly available online through www.botanicalcollections.be is valuable to scientific research as well as valorising of our collections. Currently, the site attracts 7000 users a year, which adds up to 15,000 sessions a year and the average session is more than 8 minutes. This means that people are actively using our website and these numbers can be expected to grow as we add more specimens and functionality.
The oldest Inocelliidae (Raphidioptera) from the Eocene of western North America
Authors: Makarkin Vladimir N. , Archibald S. Bruce , Jepson James E. 
Published: June 2019

AbstractOne new genus of Inocelliidae (Raphidioptera) with one new species and one undetermined specimen is described from the Eocene of North America: Paraksenocellia borealis new genus, new species from the early Eocene (Ypresian) Okanagan Highlands shale at Driftwood Canyon, British Columbia, Canada (a forewing), and Paraksenocellia species from the middle Eocene (Lutetian) of the Coal Creek Member of the Kishenehn Formation, northwestern Montana, United States of America (a hind wing). These are the oldest records of the family. The new genus possesses many character states that are rare in Inocelliidae, e.g., a very long pterostigma extending to ScP in both the forewings and hind wings; the forewing subcostal space has three crossveins; the forewing and hind wing AA1 are deeply forked; the crossvein between CuA and CuP is located far distad the crossvein 1r-m. Paraksenocellia is confidently a member of the Inocelliidae, as it possesses a proximal shift of the basal crossvein 1r-m (connecting R and M) in the forewing and the loss of the basal crossvein 1r-m in the hind wing, both apomorphies of the family. It shares some character states with the Mesozoic Mesoraphidiidae, which we consider to be mostly stem-group plesiomorphies.
Preliminary development of the Higher Education Hindrance Demands Scale amongst academics in the South African context
Authors: Dhanpat Nelesh , De Braine Roslyn , Geldenhuys Madelyn 
Published: June 2019

Orientation: Over the past two decades, since the advent of democracy in South Africa, the country has undergone transformation in virtually all sectors of society. Education is no exception, with higher education institutions (HEIs) also experiencing change. The transformation of HEIs has brought about many new challenges, demands and stresses that may hinder the work performance of academics.Research purpose: This study seeks to determine the ‘hindrance demands’ unique to the South African context by developing and validating the Higher Education Hindrance Demands Scale (HEHDS). This scale includes a set of demands placed on academics’ experiences in this context.Research approach, design and method: Data were collected from 184 academic staff members from HEIs based on a quantitative research design using a cross-sectional survey. Data were analysed through exploratory factor analysis (EFA), while the reliability of the scale was obtained through Cronbach’s coefficient alpha.Main findings: The results produced, as anticipated, a six-factor model consisting of: (1) workload, (2) higher education unrest, (3) change management, (4) decolonisation, (5) online teaching and learning and (6) psychological safety. The findings indicated excellent reliability, ranging between 0.74 and 0.90.Practical and managerial implications: Taking into consideration the context in which HEIs operate in South Africa, it is noteworthy that the recommendations in this article will assist in identifying the hindrance demands placed on academic staff. Researchers in the field are therefore called to validate the instrument developed through the use of confirmatory factor analysis.Contributions or value-add: This study adds to the limited research on hindrance demands placed on staff in HEIs.
Team-Based Learning and the Team-Based Learning Student Assessment Instrument (TBL–SAI): A Longitudinal Study of Master of Occupational Therapy Students’ Changing Perceptions
Authors: Carson Ron , Mennenga Heidi 
Published: June 2019

AbstractOBJECTIVE. Despite evidence showing that team-based learning (TBL) is accepted by students, a gap in evidence exists regarding how students’ experiences with TBL may change over time. In this study, we explored changes in accountability, satisfaction, and preference for TBL or traditional lecture among occupational therapy students using TBL across 1 yr of occupational therapy education.METHOD. Forty-one pairs of data from first- and third-term occupational therapy students were analyzed to determine changes in students’ experience with TBL over time.RESULTS. Data analysis of first- and third-term students showed significant changes in accountability with TBL (p = .027), satisfaction with TBL (p = .021), and preference for TBL over traditional lecture (p = .019).CONCLUSION. Considerable differences were found in individual cohorts regarding satisfaction, accountability, and the overall TBL experience; however, no notable differences were found between cohorts.
Reidemeister classes for coincidences between sections of a fiber bundle
Authors: Penteado Dirceu , Paiva Thales Fernando Vilamaior 
Published: June 2019

Let $s_0,f_0$ be two sections of a fiber bundle $q: E\to B$ and the coincidence set $\Gamma(s_0,f_0)\neq \emptyset$. We consider the following question: Is there $s_0\simeq_B s_1$ (by the homotopies which cover the constant homotopy $\overline{I}_B$ on the basic space) such that $\Gamma(s_1, f_0)=\emptyset$? If $b_0\in \Gamma(s_0,f_0)$ and $F_0=q^{-1}(b_0)$ is the typical fiber, in this context we can use the homotopy lifting extension propriety of the fibration $q$ to obtain homotopies over $B$. When we make this and the basic point are fixed we can use the elements $s_0(\beta), f_0(\beta^{-1})$ where $\beta \in\pi_1(B,b_0)$ and the elements $\gamma\in \pi_1(F_0,e_0)$. So we will introduce the algebraic classes of Reidemeister relative to the subgroup $\pi_1(F_0,e_0)$. When the basic points are not fixed we need to consider the classes $[\til{s}]_L$ of lifting of $s_0$ defined on the universal covering $\til{B}$ to $\til{E}$. The present work relates the lifting classes $[\til{s}]_L$ of $s_0$ and the algebraic relative Reidemeister classes $R_A(s_0,f_0; \pi_1(F_0,e_0).$