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A list of ants of the subfamilies Leptanillinae, Proceratiinae, Amblyoponinae, Ponerinae, Dorylinae, Dolichoderinae, Ectatomminae and Formicinae from Lambir Hills National Park, Sarawak, Borneo is presented with biological information. In this report 347 species in 61 genera are recorded, with 10 species newly recorded from Borneo. Literature treating species of the abovementioned subfamilies from Lambir Hills National Park is listed. In total (Part I & II), 579 species in 96 genera are recorded from this national park, representing the largest species number so far known in one place in Asia. Among the 579 species only 53% (307) are identified to species, the others no doubt including many species new to Borneo or even to science.

Limoniidae is one of the most species-rich Dipteran families, with 661 reported species in Europe. Despite the fact that the European limoniid crane fly fauna has been studied ever since Carolus Linnaeus, it is still poorly known. In this study, we summarise the taxonomic and faunistic studies of European Limoniidae, which described new species and reported first country records, between 2010 and 2020. We also report occurrence data of 244 Limoniidae species which represent the first country records or conformational records from various European countries, as we report ten species from Albania, one from Austria, thirty-seven from Belarus, five from Belgium, two from Bulgaria, two from Estonia, six from Finland, seven from France, fourteen from Greece, sixteen from Hungary, two from Iceland, six from Italy, ten from Latvia, one from Malta, nine from Montenegro, two from The Netherlands, ten from North Macedonia, forty-two from Norway, one from Poland, five from Portugal, twenty from Romania, thirty-eight from Serbia, six from Slovenia, five from Spain and seven species from Sweden for the first time. From the European territory of Russia, we report twenty-eight species from Central European Russia, seventy-two from East European Russia, fifteen from North European Russia, one from Northwest European Russia and seven from North Caucasus for the first time. Confirmatory records and corrigenda are also included.

Defining a GLAM Lab: A Galleries, Libraries, Archives and Museums (GLAM) Lab is a place for experimenting with digital collections and data. It is where researchers, artists, entrepreneurs, educators and the interested public can collaborate with an engaged group of partners to create new collections, tools, and services that will help transform the future ways in which knowledge and culture are disseminated. The exchanges and experimentation in a Lab are open, iterative and shared widely. This book describes why and how to open a GLAM Lab and encourages participation in a movement that can transform organisations and the communities they partner with. Building a GLAM Lab: Building a GLAM Lab involves defining its core values to guide future work, fostering a culture that is open, transparent, generous, collaborative, creative, inclusive, bold, ethical, accessible and encourages a mindset of exploration. The Lab should be grounded in user-centred and participatory design processes and its staff should be able to clearly communicate what the Lab is about. It's important to think big but start small and establish quick wins to get up and running. GLAM Lab teams: There are recommendations for the qualities and skills to look for in Labs teams, how to go about finding allies within and outside the institution, and ideas on how to create a nurturing environment for teams to thrive in. Labs teams have no optimal size or composition, and its team members can come from all walks of life. Teams need a healthy culture to ensure a well-functioning Lab which might be augmented intermittently by fellows, interns or researchers-inresidence. For a Lab to have lasting impact it must be integrated into the parent organisation and have the support of staff at all levels. User communities: GLAM Labs will need to engage and connect with potential users and partners. This means rethinking these relationships to help establish clear and targeted messages for specific communities. In turn, this enables Labs to adjust their tools, services and collections to establish deeper partnerships based on co-creation, and open and equal dialogue. Rethinking collections and Data: The book discusses the digital collections which are an integral part of Labs. It provides insights on how to share the collections as data, and how to identify, assess, describe, access, and reuse the collections. In addition, there is information about messy and curated data, digitisation, metadata, rights and preservation. Transformation: Experimentation is the critical core of the Lab's process. Insights about how to transform tools into operational services are demonstrated. It shows that experimentation can prepare the organisational culture and services for transformation. There is an examination of funding and the advantages and disadvantages of various models through discussion of the different mechanisms and options that an organisation can apply to Lab set-ups. Funding and Sustainability: We share insights on how to plan for a Lab's sustainability as well as a step-by-step guide for when an organisation is retiring or decommissioning a Lab. Labs have a pivotal role in the transformation of GLAMs and the book highlights the critical importance of Labs in changing the future of digital cultural heritage. Funded by UCL Qatar (Mmember of Qatar Foundation) and Qatar University Library. There has also been support from the British Library Labs, and the Library of Congress Labs.

Nucleic acid and protein sequencing-based analyses are increasingly applied to determine origin, identity and traits of environmental (biological) objects and organisms. In this context, the need for corresponding data structures has become evident. As existing schemas and community standards in the domains of biodiversity and molecular biological research are comparatively limited with regard to the number of generic and specific elements, previous schemas for describing the physical and digital objects need to be replaced or expanded by new elements for covering the requirements from meta-omics techniques and operational details. On the one hand, schemas and standards are hitherto mostly focussed on elements, descriptors, or concepts that are relevant for data exchange and publication, on the other hand, detailed operational aspects regarding origin context and laboratory processing, as well as data management details, like the documentation of physical and digital object identifiers, are rather neglected. The conceptual schema for Meta-omics Data and Collection Objects (MOD-CO; https://www.mod-co.net/) has been set up recently Rambold et al. 2019. It includes design elements (descriptors or concepts), describing structural and operational details along the work- and dataflow from gathering environmental samples to the various transformation, transaction, and measurement steps in the laboratory up to sample and data publication and archiving. The concepts are named according to a multipartite naming structure, describing internal hierarchies and are arranged in concept (sub-)collections. By supporting various kinds of data record relationships, the schema allows for the concatenation of individual records of the operational segments along a workflow (Fig. 1). Thus, it may serve as a logical and structural backbone for laboratory information management systems. The concept structure in version 1.0 comprises 653 descriptors (concepts) and 1,810 predefined descriptor states, organised in 37 concept (sub-)collections. The published version 1.0 is available as various schema representations of identical content (https://www.mod-co.net/wiki/Schema_Representations). A normative XSD (= XML Schema Definition) for the schema version 1.0 is available under http://schema.mod-o.net/MOD-CO_1.0.xsd. The MOD-CO concepts might be integrated as descriptor/element structures in the relational database DiversityDescriptions (DWB-DD) an open-source and freely available software of the Diversity Workbench (DWB; https://diversityworkbench.net/Portal/DiversityDescriptions; https://diversityworkbench.net). Currently, DWB-DD is installed at the Data Center of the Bavarian Natural History Collections (SNSB) to build an instance of its own for storing and maintaining MOD-CO-structured meta-omics research data packages and enrich them with ‘metadata’ elements from the community standards Ecological Markup Language (EML), Minimum Information about any (x) Sequence (MIxS), Darwin Core (DwC) and Access to Biological Collection Data (ABCD). These activities are achieved in the context of ongoing FAIR ('Findable, Accessible, Interoperable and Reuseable') biodiversity research data publishing via the German Federation for Biological Data (GFBio) network (https://www.gfbio.org/). Version 1.1 of the schema with extended collections of structural and operational design concepts is scheduled for 2020.

The landscape of currently existing repositories of specimen data consists of isolated islands, with each applying its own underlying data model. Using standardized protocols such as DarwinCore or ABCD, specimen data and metadata are exchanged and published on web portals such as GBIF. However, data models differ across repositories. This can lead to problems when comparing and integrating content from different systems. for example, in one system there is a field with the label 'determination', in another there is a field with the label 'taxonomic identification'. Both might refer to the same concepts of organism identification process (e.g., 'obi:organism identification assay'; http://purl.obolibrary.org/obo/OBI_0001624), but the intuitive meaning of the content is not clear and the understanding of the providers of the information might differ from that of the users. Without additional information, data integration across isolated repositories is thus difficult and error-prone. As a consequence, interoperability and retrievability of data across isolated repositories is difficult. Linked Open Data (LOD) promises an improvement. URIs can be used for concepts that are ideally created and accepted by a community and that provide machine-readable meanings. LOD thereby supports transfer of data into information and then into knowledge, thus making the data FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016). Annotating specimen associated data with LOD, therefore, seems to be a promising approach to guarantee interoperability across different repositories. However, all currently used specimen collection management systems are based on relational database systems, which lack semantic transparency and thus do not provide easily accessible, machine-readable meanings for the terms used in their data models. As a consequence, transferring their data contents into an LOD framework may lead to loss or misinterpretation of information. This discrepancy between LOD and relational databases results from the lack of semantic transparency and machine-readability of data in relational databases. Storing specimen collection data as semantic Knowledge Graphs provides semantic transparency and machine-readability of data. Semantic Knowledge Graphs are graphs that are based on the syntax of ‘Subject – Property – Object’ of the Resource Description Framework (RDF). The ‘Subject’ and ‘Property’ position is taken by URIs and the ‘Object’ position can be taken either by a URI or by a label or value. Since a given URI can take the ‘Subject’ position in one RDF statement and the ‘Object’ position in another RDF statement, several RDF statements can be connected to form a directed labeled graph, i.e. a semantic graph. Semantic Knowledge Graphs are graphs in which each described specimen and its parts and properties possess their own URI and thus can be individually referenced. These URIs are used to describe the respective specimen and its properties using the RDF syntax. Additional RDF statements specify the ontology class that each part and property instantiates. The reference to the URIs of the instantiated ontology classes guarantees the Findability, Interoperability, and Reusability of information contained in semantic Knowledge Graphs. Specimen collection data contained in semantic Knowledge Graphs can be made Accessible in a human-readable form through an interface and in a machine-readable form through a SPARQL endpoint (https://en.wikipedia.org/wiki/SPARQL). As a consequence, semantic Knowledge Graphs comply with the FAIR guiding principles. By using URIs for the semantic Knowledge Graph of each specimen in the collection, it is also available as LOD. With semantic Morph·D·Base, we have implemented a prototype to this approach that is based on Semantic Programming. We present the prototype and discuss different aspects of how specimen collection data are handled. By using community created terminologies and standardized methods for the contents created (e.g. species identification) as well as URIs for each expression, we make the data and metadata semantically transparent and communicable. The source code for Semantic Programming and for semantic Morph·D·Base is available from https://github.com/SemanticProgramming. The prototype of semantic Morph·D·Base can be accessed here: https://proto.morphdbase.de.

Transparency as well as complete and traceable documentation of specimens, samples and associated information are prerequisites to comply with laws and regulations in Provider and User Countries to ensure benefits of utilised genetic resources are shared. Besides legal compliance, these measures should also help to build trust among users, suppliers and collaborators. This concerns for example laws of providing countries that have established access laws under the Nagoya Protocol, such as Mexico's Ley General de la Vida Silvestre, or under the Convention of Biological Diversity (CBD), such as the Brazil Law No. 13,123, regulating access to the country's genetic heritage. On the other hand there are laws and regulations in user countries that ensure compliance with access laws of providing countries under the Nagoya protocol (e. g. Regulation (EU) No 511/2014 of the European Parliament and of the Council of 16. April 2014, which has to be implemented at national level in the EU member states). As an institution holding genetic resources as living collections (including seeds), herbarium specimens and DNA and tissue samples as well as a wealth of associated data, the Botanic Garden and Botanical Museum Berlin (BGBM) aims at creating transparency on the processes associated with the handling of these materials and data, such as metadata or associated analytical research results (Fig. 1). As a member of CETAF (Consortium of European Taxonomic Facilities), IPEN (International Plant Exchange Network), and GGBN (Global Genome Biodiversity Network) the workflows and documentation of biological collections at BGBM are in compliance with CETAF’s Code of Conduct on Access and Benefit Sharing (https://www.cetaf.org/services/natural-science-collections-and-access-and-benefit-sharing) for herbarium specimens including algae, IPEN´s Code of Conduct (http://www.bgci.org/policy/ipen) for living plants and seeds, and GGBN’s (https://library.ggbn.org/share/s/UM5JietQR9aevtYDymHbjw) code of conduct (CoC) for DNA and tissue samples. A major challenge was to ensure the comprehensive, transparent, and traceable documentation of specimens and associated material and information along our internal workflows that have evolved with the development of a manifold of protocols. However, this challenge presented the opportunity to revise the existing protocols that cover the handling, collecting, and processing of the specimens, which had accumulated over the long history of our collections and our databases, into a consistent set of workflows (Stevens et al. 2019). A key component is the Collection Data Form (CDF) which guarantees that all necessary documentation will be imported into our collection management systems, including potential restriction of destructive sampling or loaning. The latest version of the CDF as wells as other information about BGBM’s collection and data workflows can be found at https://wiki.bgbm.org/collectionworkflows. This site has been created in late 2018 to be used by everyone to explore BGBM’s routines and examples. All legal and formal documents, such as collecting permits, Prior Informed Consent (PIC), CITES documentation, phytosanitary documents, Material Transfer Agreement (MTA), long term agreements with partner institutions, project agreements etc., are digitized and managed using a digital asset management software (Alfresco, an open source document management system, www.alfresco.com. The challenge is to link all this information unambiguously. To achieve this, all of BGBM’s collections (tissue and DNA-samples as well as living plants, seeds and diatom cultures) must have, whenever possible, a herbarium specimen that can be permanently stored and that allows a correct taxonomic identification of the material. These voucher specimens shall be digitized. Therefore, all data and all documentation can be traced back to identifiers referring to a herbarium specimen or living accession. The other collections (e. g. DNA and tissue samples) also get unique identifiers that are cross-referenced with each other. By this process we ensure that genetic material is identified by collecting number (assigned by the collector), as well as accession number and barcodes (assigned by BGBM according to type of material such as plant tissue, DNA, sample, seed lot, plant accession number of living plants, environmental sample, culture strain or herbarium specimen), and, most importantly in this regard, the document number/identifier of the legal or formal documents issued by the national legal entity or signed by a partner institution (assigned by the documentation office at BGBM when digitized). All these identifiers or numbers must refer to the respective material and as such allow BGBM to trace back the material at all stages of processing. This is of vital importance when genetic material is exchanged with partners. If there are any restrictions or any requirements for the further processing, use or exchange of plant material this is documented and flagged in all database modules and accession numbers.

Species distribution models (SDMs) are often calibrated using presence- only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially-explicit knowledge of sampling effort is rarely available. In multi-species studies, sampling effort has been inferred following the target-group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species- specific response to this type of bias correction. The present study aims at evaluating the impacts of sampling bias and bias correction on SDM performance. To this end, we designed a realistic system of sampling bias and virtual species based on 92 terrestrial mammal species occurring in the Mediterranean basin. We manipulated presence and background data selection to calibrate four SDM types. Unbiased (unbiased presence data) and biased (biased presence data) SDMs were calibrated using randomly distributed background data. We used real and TG-estimated sampling efforts in background selection to correct for sampling bias in presence data. Overall, environmental sampling bias had a deleterious effect on SDM performance. In addition, bias correction improved model accuracy, and especially when based on spatially-explicit knowledge of sampling effort. However, our results highlight important species-specific variations in susceptibility to sampling bias, which were largely explained by range size: widely-distributed species were most vulnerable to sampling bias and bias correction was even detrimental for narrow-ranging species. Furthermore, spatial discrepancies in SDM predictions suggest that bias correction effectively replaces an underestimation bias with an overestimation bias, particularly in areas of low sampling intensity. Thus, our results call for a better estimation of sampling effort in multispecies system, and cautions the uninformed and automatic application of TG bias correction. This article is protected by copyright. All rights reserved.

Palaeontology and biology are closely related sciences, as are the collections associated with them. Nevertheless there are differences between the two types of collections and the scientific data that they yield with regards to taxonomy, climate and ecology. In order to bridge the gap between the two subjects, it is important to clarify what these differences are and how they can be used to supplement research that addresses future environmental/climatic issues. In biology, valuable traits of the whole organism serve for taxonomy. In the fossil record, a morphospecies concept needs to be used because specimens are mainly preserved fragmentarily and palaeontologists have to take advantage of morphological traits that are often disregarded by biologists. Another difference is that biological objects represent modern time, while the fossil record provides valuable information on a deep time perspective, i.e., in a third dimension. Yet, these two disciplines obviously depend on each other: while biologists provide palaeontologists with information about unfossilised soft parts, palaeontology can help to solve questions about life in the past. Using four current case studies from the Stuttgart Natural History Museum, we provide examples of how biological and palaeontological information stored in museum collections are linterlinked, and particularly how palaeontology can help to solve current and future problems. We also highlight the potential of palaeontological collections and demonstrate the necessity of digitizing large quantities of objects as well as the related basic information. Case studies are: Fossil leaves provide evidence for past atmospheric CO2 levels and climate change, which can be used for climate change models. Fossils help to understand current and future hazards e.g., fossils embedded in tsunami sediments can provide information on how tsunamis affect shelf marine ecosytems. Extensive taxonomic studies of Miocene land snails and the comparison with extant relatives allow the reconstruction of fossil environments. Combined with complementary methods, the biological, geological and meteorological factors controlling these environments can be reconstructed. Phylogenetic studies tell us how life evolved and how organisms have changed through time. An important factor for phylogeny is the time-aspect, such as the splitting of lineages. Phylogenetic trees based on modern taxa can only be validated by fossils. We will present an example of insect phylogeny. Fossil leaves provide evidence for past atmospheric CO2 levels and climate change, which can be used for climate change models. Fossils help to understand current and future hazards e.g., fossils embedded in tsunami sediments can provide information on how tsunamis affect shelf marine ecosytems. Extensive taxonomic studies of Miocene land snails and the comparison with extant relatives allow the reconstruction of fossil environments. Combined with complementary methods, the biological, geological and meteorological factors controlling these environments can be reconstructed. Phylogenetic studies tell us how life evolved and how organisms have changed through time. An important factor for phylogeny is the time-aspect, such as the splitting of lineages. Phylogenetic trees based on modern taxa can only be validated by fossils. We will present an example of insect phylogeny. These case studies not only show how biology and palaeontology are interlinked, but the first three studies are sound examples of how the knowledge of the past helps to understand the present. Furthermore, the first two studies are highly relevant for predicting the future. All of this information can only be used appropriately, if large proportions of data are available that include information on geology and age. For this reason, the Access to Biological Collection Data Extended for Geosciences (ABCD EFG) standard is so important, as it extends the two-dimensional view (Recent) into a third dimension (deep time). Our vision is an integrated modelling of past, present and future scenarios, whether for climate or ecosystem change, or geological hazards. Considering the deep time information, we can model how changes would take place under natural conditions, i.e., without anthropogenic influence. This requires the availability of large data sets of taxonomic information on the EFG level from all over the world.

The TDWG standard ABCD (Access to Biological Collections Data task group 2007) was aimed at harmonizing terminologies used for modelling biological collection information and is used as a comprehensive data format for transferring collection and observation data between software components. The project ABCD 3.0 (A community platform for the development and documentation of the ABCD standard for natural history collections) was financed by the German Research Council (DFG). It addressed the transformation of ABCD into a semantic web-compliant ontology by deconstructing the XML-schema into individually addressable RDF (Resource Description Framework) resources published via the TDWG Terms Wiki (https://terms.tdwg.org/wiki/ABCD_2). In a second step, informal properties and concept-relations described by the original ABCD-schema were transformed into a machine-readable ontology and revised (Güntsch et al. 2016). The project was successfully finished in January 2019. The ABCD 3 setup allows for the creation of standard-conforming application schemas. The XML variant of ABCD 3.0 was restructured, simplified and made more consistent in terms of element names and types as compared to version 2.x. The XML elements are connected to their semantic concepts using the W3C SAWSDL (Semantic Annotation for Web Services Description Language and XML Schema) standard. The creation of specialized applications schemas is encouraged, the first use case was the application schema for zoology. It will also be possible to generate application schemas that break the traditional unit-centric structure of ABCD. Further achievements of the project include creating a Wikibase instance as the editing platform, with related tools for maintenance queries, such as checking for inconsistencies in the ontology and automated export into RDF. This allows for fast iterations of new or updated versions, e.g. when additional mappings to other standards are done. The setup is agnostic to the data standard created, it can therefore also be used to create or model other standards. Mappings to other standards like Darwin Core (https://dwc.tdwg.org/) and Audubon Core (https://tdwg.github.io/ac/) are now machine readable as well. All XPaths (XML Paths) of ABCD 3.0 XML have been mapped to all variants of ABCD 2.06 and 2.1, which will ease transition to the new standard. The ABCD 3 Ontology will also be uploaded to the GFBio Terminology Server (Karam et al. 2016), where individual concepts can be easily searched or queried, allowing for better interactive modelling of ABCD concepts. ABCD documentation now adheres to TDWG’s Standards Documentation Standard (SDS, https://www.tdwg.org/standards/sds/) and is located at https://abcd.tdwg.org/. The new site is hosted on Github: https://github.com/tdwg/abcd/tree/gh-pages.

Semantic annotations of datasets are very useful to support quality assurance, discovery, interpretability, linking and integration of datasets. However, providing such annotations manually is often a time-consuming task . If the process is to be at least partially automated and still provide good semantic annotations, precise information extraction is needed. The recognition of entity names (e.g., person, organization, location) from textual resources is the first step before linking the identified term or phrase to other semantic resources such as concepts in ontologies. A multitude of tools and techniques have been developed for information extraction. One of the big players is the text mining framework GATE (Cunningham et al. 2013) that supports annotation rules, semantic techniques and machine learning approaches. We will run GATE's default ANNIE pipeline on collection datasets to automatically detect persons, locations and time. We will also present extensions to extract organisms (Naderi et al. 2011), environmental terms, data parameters and biological processes and how to link them to ontologies and LOD resources, e.g., DBPedia (Sateli and Witte 2015). We would like to discuss the results with the conference participants and welcome comments and feedbacks on the current solution. The audience is also welcome to provide their own datasets in preparation for this session.

Plačevanje davkov je privilegij davkoplačevalcev in države, saj ravno davki so nujno potrebni za delovanje države. V delu ugotavljamo ali davčni zavezanci prilagajo svoje davčne osnove. Paying taxes are privilege for taxpayers and the state. Taxes are necessary for functioning the state. In our work we note if taxpayers paying or adapt their tax base.

The ready availability of online maps of plant and animal collection locations has drawn attention to the need for georeference accuracy. Many obvious georeference errors, for example, that map land animals over sea, the wrong hemisphere, or the wrong country, may be avoided if collectors and data providers could easily map their data points prior to publication. Various tools are available for quality control of georeference data, but many involve an investment of time to learn the software involved. This paper presents a method for the rapid map display of longitude and latitude data using the chart function in Microsoft Office Excel®, arguably the most ubiquitous spreadsheet software. Advantages of this method include: immediate visual feedback to assess data point accuracy; and results that can be easily shared with others. Methods for making custom Excel chart maps are given, and we provide free charts for the world and a selection of countries at http://www.vectormap.org/resources.htm.