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Un paseo vertiginoso y pausado, a dos siglos y medio de historia del Jardín, de Botánica y de Ciencia

Plinian Core (PliC) is a set of vocabulary terms that can be used to describe different aspects of biological species information. Under "biological species information" all kinds of properties or traits related to taxa—biological and non-biological—are included. Thus, for instance, terms pertaining to descriptions, legal aspects, conservation, management, demographics, nomenclature, or related resources are incorporated. Plinian Core started as a collaborative project between Instituto Nacional de Biodiversidad (INBio, Costa Rica) and the Global Biodiversity Information Facility Spain (GBIF Spain) in 2005. In successive years, the National Commission for Knowledge and Use of Biodiversity (CONABIO, Mexico), the National Network of Open Biodiversity Data (SiB Colombia)/the Humboldt Institute (Colombia), the University of Granada (Spain) and the University of São Paulo (USP, Brazil) were also involved. In 2012. PliC aligned with the Biodiversity Information Standards (TDWG) and an umbrella Interest Group was created (Species Information IG, Pando 2017b) with developments carried out by its corresponding TDWG Task Group (Plinian Core Task Group 2020b). PliC design requirements include: ease of use, being self-contained, able to support data integration from multiple databases, and ability to handle different levels of granularity. Since its early stages, PliC was formalized as an XML Schema. Its terms can be grouped in its current version as follows: Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data During its years of existence, PliC has been implemented in several real-life contexts (e.g., Spanish Ministry for the Ecological Transition and the Demographic Challenge 2020, CRBIO 2020, SIB Colombia 2020), and a set of extensions for using PliC in GBIF Integrated Publishing Toolkit (IPT) was developed (Plinian Core Task Group 2020a). PliC has been featured in some TDWG conferences, either to explore and explain how it relates to other TDWG standards (Pando 2018), how PliC handles specific information aspects (Pando 2017a), or how it may be used in biodiversity portals (Vargas et al. 2018, Vargas et al. 2019). Recent work has focused on how to represent terms and codify structure as an XML Schema (XSD) under the current TDWG Documentation Standard (SDS, Vocabulary Maintenance Specification Task Group 2017). Challenges identified in the process comprise: how to handle terms used in different contexts, how to preserve the semantic context of terms reflected in the XSD hierarchy in the SDS flat layout, and how to refer to terms and definitions defined in other data specifications. These are the areas to focus our contribution.

Biodiversity informatics is a new and evolving field, requiring efforts to develop capacity and a curriculum for this field of science. The main objective was to summarise the level of activity and the efforts towards developing biodiversity informatics curricula, for work-based training and/or academic teaching at universities, taking place within the Global Biodiversity Information Facility (GBIF) countries and its associated network. A survey approach was used to identify existing capacities and resources within the network. Most of GBIF Nodes survey respondents (80%) are engaged in onsite training activities, with a focus on work-based professionals, mostly researchers, policy-makers and students. Training topics include data mobilisation, digitisation, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data that currently reside as scattered datasets. An initial assessment of academic teaching activities highlighted that countries in most regions, to varying degrees, were already engaged in the conceptualisation, development and/or implementation of formal academic programmes in biodiversity informatics, including programmes in Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. Digital e-learning platforms were an important tool to help build capacity in many countries. In terms of the potential in the Nodes network, 60% expressed willingness to be recruited or commissioned for capacity enhancement purposes. Contributions and activities of various country nodes across the network have been highlighted and a working curriculum framework has been defined.

Citizen science projects seem to have a high potential to provide systematized, high-quality biodiversity observations for science and other purposes. iNaturalist offers users purposeful participation by creating projects that allow observations to be grouped with a taxonomic and geographic focus, as well as on a given time scale. Between 2014 and December 2021, 720 iNaturalist projects have been created for Spain, most of them after the establishment of Natusfera—the Spanish branch of the iNaturalist global Community—in 2020. In this paper, we analyze how iNaturalist projects are created; how they perform in terms of engagement, data contribution, and impact; and assess the degree and possible causes of their success. A database with project descriptors and indicators was created for this purpose. We discovered that a high percentage of the projects (more than 25%) perform far short of expectations, and that bioblitzes are in general very successful in terms of creation, dissemination, and participation. Finally, we present some recommendations aiming to make these projects more effective., Ministerio de Ciencia e Innovación

This article describes the herbarium database of the University of Lomé. The database provides a good representation of the current knowledge of the flora of Togo. The herbarium of University of Lomé, known also as Herbarium togoense is the national herbarium and is registered in Index Herbariorum with the abbreviation TOGO. It contains 15,000 specimens of vascular plants coming mostly from all Togo's ecofloristic regions. Less than one percent of the specimens are from neighbouring countries such as Ghana, Benin and Burkina Faso. Collecting site details are specified in more that 97% of the sheet labels, but only about 50% contain geographic coordinates. Besides being a research resource, the herbarium constitutes an educational collection. The dataset described in this paper is registered with GBIF and accessible at https://www.gbif.org/dataset/b05dd467-aaf8-4c67-843c-27f049057b78. It was developed with the RIHA software (Réseau Informatique des Herbiers d'Afrique). The RIHA system (Chevillotte and Florence 2006, Radji et al. 2009) allows the capture of label data and associated information such as synonyms, vernacular names, taxonomic hierarchy and references.

This dataset contains information on the presence and distribution of sea turtles in Togo. Observations were carried out through a network of ten ecoguards (local guides), facilitated by five fishermen, and coordinated by a field technician, all under the supervision of a scientific coordinator. Data on the occurrence or direct observation of sea turtles on the Togolese coast from September 2012 to August 2013 is presented based on 740 occurrences.

Biodiversity informatics has been characterized as a rapidly growing interdisciplinary field, which aims to bring together the areas of biodiversity and informatics. A study was conducted looking at the current level of activity within the GBIF Participant countries and its associated network in relation to work-based training and/or academic teaching at universities, in the field of biodiversity informatics. It was intended to get an overview of GBIF Node Managers, (hence, member countries), already engaged in developing course curricula, or in providing training, and whether they would be willing to share resources or enter into collaborations, to further elaborate this field of science. This investigation followed a survey approach, conducted globally across the GBIF community to identify the existing capacities and resources within the network. The results indicated that the vast majority of GBIF Nodes survey respondents, are engaged in onsite training activities in biodiversity informatics areas, with a focus on professionals, mostly researchers, policy makers and students. Training includes data digitization, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data which currently resides as scattered databases/datasets. A list containing the associated URL's for training and dissemination activities in GBIF Nodes has been developed, based on survey results, and will be presented. An initial assessment of the academic teaching activities indicated that many countries across most regions were already engaged in the conceptualisation, development and/or implementation of formal academic programs in biodiversity informatics including Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. This study also identified that digital e-learning platforms were a very important tool to help build capacity in a number of countries. To assess the level of potential in the network to support academic teaching and work-based training, sixty percent indicated that they would be willing to be recruited or commissioned to support teaching activities, demonstrating the value of the Nodes network to support the development of biodiversity informatics globally. The contributions and activities of various nodes across the network will be highlighted and a working high-level curriculum framework will be discussed.

DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry, Wohner et al. 2019) is one of the largest repositories of long-term ecological research (LTER) datasets. It provides sophisticated searching tools by metadata elements and identifiers for all the 930 contained datasets, most of them from European sites. Whereas datasets' metadata are highly structured and searchable, datasets themselves have little standardization in terms of content, identifiers or license, making data integration difficult or cumbersome. Adopting the data FAIR guiding principles(Wilkinson et al. 2016) for LTER data would result in better data integration and reutilization to support knowledge discovery and innovation in ecological research. The Global Biodiversity Information Facility (GBIF 2019a). is the largest repository of species distribution data in the world, providing access to more than a billion records from over 43,000 datasets. GBIF is a good example of FAIR principles implementation: GBIF data is highly standardized, using Darwin Core (Wieczorek et al. 2012) for data and ecological metadata language (EML, Fegraus et al. 2005) for metadata, allowing record-level search; and has implemented globally unique and persistent identifiers for datasets and downloads. Relevant in this context is that GBIF has recently introduced a new data format intended for monitoring projects and sampling event protocols (GBIF 2019b). In this presentation, we explore the suitability of GBIF data formats and workflows to serve LTER datasets, and the work it may take to transform typical LTER datasets into these formats. For this exercise, we use some datasets available via the DEIMS platform, corresponding to the same territory, (Sierra Nevada, Spain (e.g. Bonet 2016, Bonet 2018) and transform them into the GBIF's sample-based Event core publish them in the GBIF data network, and then perform an analysis to assess how the standardized datasets work in practice, both among themselves and also with typical “occurrence-based” GBIF datasets. Finally, we discuss our findings and make recommendations for the GBIF and LTER communities.

Resumen del trabajo presentado a la Conferencia Bio Diversity Next, celebrada en Leiden (Países Bajos) del 22 al 25 de octubre de 2019., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

The dataset contains detailed distribution information on several fungal groups. The information has been revised, and in many times compiled, by expert mycologist(s) working on the monographs for the Flora Mycologica Iberica Project (FMI). Records comprise both collection and observational data, obtained from a variety of sources including field work, herbaria, and the literature. The dataset contains 59,235 records, of which 21,393 are georeferenced. These correspond to 2,445 species, grouped in 18 classes. The geographical scope of the dataset is Iberian Peninsula (Continental Portugal and Spain, and Andorra) and Balearic Islands. The complete dataset is available in Darwin Core Archive format via the Global Biodiversity Information Facility (GBIF).

Reliable plant species identification from seeds is intrinsically difficult due to the scarcity of features and because it requires specialized expertise that is becoming increasingly rarer, as the number of field plant taxonomists is diminishing (Bacher 2012, Haas and Häuser 2005). On the other hand, seed identification is relevant in some science domains such as plant community ecology, archaeology, paleoclimatology. Besides, economic activities such as agriculture, require seed identification to assess weed species contained in the "soil seed banks" (Colbach 2014) to enable targeted treatments before they become a problem. In this work, we explore and evaluate several approaches by using different training image sets with various requisites and assessing their performance with test datasets of different sources. The core training dataset is provided by the Anthos project (Castroviejo et al. 2017) as a subset of its image collection. It consists of nearly a 1000 images of seeds identified by experts. As identification algorithm, we will use state-of-the-art convolutional neural networks for image classification (He et al. 2016). The framework is fully written in Python using the TensorFlow (Abadi et al. 2016) module for deep learning., EU Horizon 2020 framework programme project DEEP-Hybrid-Datacloud (Grant Agreement number 777435)

Species level information, as an important component of the biodiversity information landscape, is an area where some TDWG standards and activities, coincide. Plinian Core (Plinian Core Task Group 2018) is a generalistic specification that covers aspects such species descriptions and nomenclature, as well as many others (legal, conservation, management, etc.). While the Plinian Core non-biological terms have no counterpart in the TDWG developments, some of its biological ones have, and that is the focus of this work. First, it must be noticed that Plinian Core relies on some TDWG standards for specific facets of species information: Standard: Darwin Core (Darwin Core maintenance group, Biodiversity Information Standards (TDWG) 2014) Elements: taxonConceptID, Hierarchy, MeasurementOrFact, ResourceRelationShip. Standard:Ecological Metadata Language (EML project members 2011) Elements: associatedParty, keywordSet, coverage, dataset Standard:Encyclopedia of Life Schema (EOL Team 2012) Elements: AncillaryData: DataObjectBase Standard:Global Invasive Species Network (GISIN 2008) Elements: origin, presence, persistence, distribution, harmful, modified, startValidDate, endValidDate, countryCode, stateProvince, county, localityName, county, language, citation, abundance... Standard:Taxon Concept Schema. TCS (Taxonomic Names and Concepts interest group 2006) Elements: scientificName Given the direct dependency of Plinian Core for these terms, they do not pose any compatibility or interoperability problem. However, biological descriptions --especially structured ones-- are the object of DELTA (Dallwitz 2006) and the Structured Descriptive Data (SDD) (Hagedorn et al. 2005), and also covered by Plinian Core. This convergence presents overlaps, mismatches and nuances, which discussion is the core of this work. Using some species descriptions as a test case, and transforming them between these standards (Plinian Core, DELTA, and SDD), the strengths and compatibility issues of these specifications are evaluated and discussed. Some operational aspects of Plinian Core in relation to GBIF's IPT (GBIF Secretariat 2016) and the INSPIRE directive (European Commission 2007) are also reviewed.

Plinian Core is a set of vocabulary terms that can be used to describe all kind of properties related to taxa (https://github.com/tdwg/PlinianCore). "Interactions" is a class of properties included in the "Natural History" class. In its current state, the class comprises the elements taken from the Darwin Core class "ResourceRelationship", these are: resourceRelationshipID, resourceID, relatedResourceID, relationshipOfResource, relationshipAccordingTo, relationshipEstablishedDate, relationshipRemarks These terms are complemented with a Plinian Core native element: InteractionSpeciesType (see https://github.com/tdwg/PlinianCore/wiki/InteractionAtomizedClass), intended to group all possible interactions into logical categories. As a generic standard, Plinian Core recommends but does not impose the use of controlled vocabularies to specify interactions and their types. However, the community would benefit enormously from having some consensus vocabularies to recommend for interactions and categories of interactions. That is --as we see it-- the frontier in managing species interaction information. We will review some of the investigations on this subject within the scope of Plinian Core.

When making an initial assessment of a dataset originating from an unfamiliar source, a user typically relies on the visible properties of the dataset as a whole, such as, the title, the publisher, and the size of the dataset. Aspects of data quality are usually out of view, beyond some intuitions and hard to compare assertions. In 2007 at GBIF Spain we tried to correct that by developing an index that enables a user to assess the quality of Darwin Core datasets published by GBIF-Spain, and to track improvements in quality over time. Our goal was to create an index that is explicit, easy to understand, and easy to obtain. We dubbed that index "ICA" GBIF Spain (2010) for its name in Spanish "Índice de Calidad Aparente" (Apparent Quality Index). We say ICA measures "apparent quality", because, although unlikely, a dataset can have a high ICA, while its records are actually a poor reflection of the reality to which they refer. ICA summarizes data quality on the three primary dimensions of biodiversity data: taxonomic, geospatial and temporal. In this contribution we will present the rationale behind the ICA, how it is calculated, how it works within the Darwin Test tool Ortega-Maqueda and Pando (2008), how it is integrated in the data publication processes of GBIF Spain, and some discussion and results about its utility and potential. We also compare ICA to the emerging framework for data quality assessmentTDWG Data Quality Interest Group (2016).

In this paper we collect the 103 algae names published by the Spanish phycologist Pedro González Guerrero during his life-long study of algae at the Royal Botanic Garden of Madrid. Names are presented with specific information to facilitate the typification of these names in the future, since P. González only used small illustrations as type elements when describing new infrageneric taxa. We also review here the status of this collection, which has been restored and databased in the process. The collection currently comprises over 2150 specimens, a fraction of what it was a much larger one., En este trabajo reunimos los 103 nombres de alga que el ficólogo español Pedro González Guerrero publicó a lo largo de toda una vida científica dedicada al estudio de este grupo en el Real Jardín Botánico de Madrid. Revisamos el estado de su colección, la cual ha sido restaurada y regis-trada en una base de datos, y proporcionamos información detallada de los nombres y ejemplares para facilitar la tipificación en el futuro, puesto que P. González Guerrero se limitaba a usar pequeñas ilustraciones como elementos tipo al describir nuevos táxones infraespecíficos. La colección actual abarca más de 2150 muestras y es solo una fracción de la original.

Geranium L. is a genus of over 350 species distributed throughout most of the world, except in lowland tropical areas. It is the largest genus of the Geraniaceae and is represented in the New World by 137 species. This dataset includes 8,937 records that covers the genus Geranium the New World, providing an updated, taxonomically consistent and a sound geographical distribution of the 137 species of Geranium in America. Specimens from 128 herbaria were reviewed. These were supplemented by others collected during nine field trips, which allowed better knowledge of the variability of characters within populations, and refining species distribution ranges. Each record represents a specimen that has been reviewed and in some cases collected by C. Aedo. Accepted scientific name, locality details, distribution status (introduced, native, naturalized, uncertain), geographic coordinates are given for 8,538 (95%) records, and habitat information for 3,952 (44%). All data have been released under a CC-BY license in a standardized format, which enables easy integration with other data, for example through GBIF.org.

We are currently studying the feasibility of applying deep learning techniques to natural sciences. In this contribution we will show our recent advances with an easy plug-and-play framework (that uses the Lasagne module built on top of Theano), which we have successfully trained for plant identification. Subsequent trials have been carried out on cone snails (Conus spp.) with minimum overhead and without writing any new code. The fact that these applications share a common API makes it very easy to create new applications (e.g., on Android, as we are currently testing) and to apply them to new species groups. The code for the framework can be found at: https://github.com/ IgnacioHeredia/plant_classification. This kind of application makes taxonomic expertise directly accessible to members of the general public interested in nature and the diversity of living organisms. These applications have a clear educational impact, and may also be used to enhance conservation actions. Deployment and use of the currrent framework is supported by the recently begun, EUfunded project >DEEP Hybrid Datacloud>. In particular, the project will support the extensive training of the system needed to develop new applications, and will provide the necessary computational resources to the users.

In order to effectively understand and cope with the current ‘biodiversity crisis’, having large-enough sets of qualified data is necessary. Information facilitators such as the Global Biodiversity Information Facility (GBIF) are ensuring increasing availability of primary biodiversity records by linking data collections spread over several institutions that have agreed to publish their data in a common access schema. We have assessed the primary records that one such publisher, the Spanish node of GBIF (GBIF.ES), hosts on behalf of a number of institutions, considered to be a highly representative sample of the total mass of available data for a country in order to know the quantity and quality of the information made available. Our results may provide an indication of the overall fitness-for-use in these data. We have found a number of patterns in the availability and accrual of data that seem to arise naturally from the digitization processes. Knowing these patterns and features may help deciding when and how these data can be used. Broadly, the error level seems low. The available data may be of capital importance for the development of biodiversity research, both locally and globally. However, wide swaths of records lack data elements such as georeferencing or taxonomical levels. Although the remaining information is ample and fit for many uses, improving the completeness of the records would likely increase the usability span for these data.

This study focuses on embeddable algorithms that operate within multi-scale wireless sensor networks for damage detection in civil infrastructure systems, and in specific, the Bivariate Regressive Adaptive INdex (BRAIN) to detect damage in structures by examining the changes in regressive coefficients of time series models. As its name suggests, BRAIN exploits heterogeneous sensor arrays by a data-driven damage feature (DSF) to enhance detection capability through the use of two types of response data, each with its own unique sensitivities to damage. While previous studies have shown that BRAIN offers more reliable damage detection, a number of factors contributing to its performance are explored herein, including observability, damage proximity/severity, and relative signal strength. These investigations also include an experimental program to determine if performance is maintained when implementing the approaches in physical systems. The results of these investigations will be used to further verify that the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

A checklist of the genus Geranium L. (Geraniaceae) is presented. Four hundred and twenty three species are recognized in 3 subgenera and 18 sections. Our classification differs from Yeo's only in some aspects of subg. Erodioidea and Geranium. Section Brasiliensia is included in subg. Erodioidea, and sect. Neurophyllodes, Paramensia and Azorelloida in subg. Geranium. Section Azorelloida is proposed as an avowed substitute (nom. nov.) for sect. Petraea R. Knuth, nom. illeg. G. collae is proposed as avowed substitute (nom. nov.) for G. intermedium Colla, nom. illeg. An identification key to subgenera and sections is presented. A thorough revision of available names in the genus, and a review of their nomenclatural status were carried out. Correct name, place of publication and distribution are given for each species. Geographical distributions are given at region and botanical country leveis following the International Working Group on Taxonomic Databases (TDWG) standard. When possible, selected references with relevant information are also included. Se presenta una "checklist" del género Geranium L. (Geraniaceae) en la que se aceptan 423 especies, repartidas en 3 subgéneros y 18 secciones. Seguimos la clasificación propuesta por Yeo, aunque reconocemos la sección Brasiliensia en el subg. Erodioidea, y las secciones Neurophyllodes, Paramensia y Azorelloida en el subg. Geranium. La sect. Azorelloida es propuesta como nombre nuevo para la sect. Petraea R. Knuth, nom. illeg. Asimismo se propone G. collae como nombre nuevo para G. intermedium Colla, nom. illeg. Se incluye una clave para la identificación de los subgéneros y secciones. Después de revisar la práctica totalidad de los nombres publicados en Geranium se da el nombre correcto, el lugar de publicación y el área de distribución de cada especie aceptada, así como las referencias bibliográficas más importantes para cada una de ellas. Para codificar las distribuciones geográficas, en los niveles de "región" y "país botánico", se han seguido las propuestas del International Working Group on Taxonomic Databases (TDWG).