Public Repositories Research Articles

Knowledge Infrastructures Require Scaffolding

Watch VIDEO. Knowledge infrastructures are multi-faceted, ever-shifting networks of people and information. They span departments, address varying needs and, ideally, provide stability despite personnel turnover and other changes. Information is made explicit and available via organizational charts, procedural manuals and regular meetings, ensuring members of an institution are kept up-to-date with necessary information. Moving below and between these formal channels, however, is the informal network, a web of unquantifiable, personal relationships, which are nevertheless vital to the functioning of the system. Developing and maintaining a system that works for the current and future needs of diverse groups—e.g., a publication repository used by administrative and research staff—involves tapping into the existing knowledge infrastructure at multiple points, in both the formal departments and the spaces between them. In addition to the stakeholder meetings and the lists of user requirements, we have the informal conversations where we can learn the tacit information, for example, how research groups are defined by different parties and whether software versions are considered separate publications. It takes time to learn the right questions and to learn who might hold the answers or, if there is no pre-existing solution, with whom you can work to establish one. In this session, we will use the example of a publication repository to discuss the role of personal relationships throughout a project lifecycle, from conception through rollout and ongoing updates. I invite you to consider how to support the development of the informal network, how to identify tacit knowledge and information gaps, and how to take personality and communication skills into account when hiring for technical positions.

Integration of primary contact physiotherapists in the emergency department for individuals presenting with minor musculoskeletal disorders: Protocol for an economic evaluation.

1) To compare the average cost of an emergency department (ED) visit for various minor musculoskeletal disorders between two models of care (physiotherapist and ED physician or ED physician alone); 2) To evaluate the incremental cost-effectiveness ratio (ICER) of these two models of care over a 3-month period post-initial visit; and 3) To estimate the ICER of three ED models of care (physiotherapist and ED physician, ED physician alone, physiotherapist alone) over a two-year period. Obj.1: The costs incurred by participants in the two groups during their ED visit will be calculated using the Time-Driven Activity-Based Costing (TDABC) method. These costs will be compared using generalized linear models. Obj. 2: The ICER of the two models will be evaluated over three months via a cost-utility analysis that will combine costs and effectiveness data (quality-adjusted life years) using both Health system and Societal perspectives (patient + health system costs). Obj. 3: The 2-year ICER of the three above-mentioned models will be estimated using a mathematical model including a decision tree (0-3 months post-visit) and a Markov model (3-24 months post-visit), also using both Health system and Societal perspectives. Data to answer the three objectives will come from data collected during a randomized clinical trial (n = 78, CHU de Québec)which will be supplemented with data obtained via some of the CHU de Québec administrative databases (nominative data; SIURGE (ED management software), Cristal-Net (patient electronic record), and the ED's pharmacy transactions directory; administrative data: drug costs repository), the literature, and public cost repositories. This study will help to determine which model of care is most efficient for the management of individuals who come to the ED with minor musculoskeletal disorders. The increased involvement of various health professionals in the management of patients in the ED paves the way for the development of new avenues of practice and more efficient organization of services.

Global building exposure model for earthquake risk assessment

The global building exposure model is a mosaic of local and regional models with information regarding the residential, commercial, and industrial building stock at the smallest available administrative division of each country and includes details about the number of buildings, number of occupants, vulnerability characteristics, average built-up area, and average replacement cost. We aimed for a bottom-up approach at the global scale, using national statistics, socio-economic data, and local datasets. This model allows the identification of the most common types of construction worldwide, regions with large fractions of informal construction, and areas prone to earthquakes with a high concentration of population and building stock. The mosaic of exposure models presented herein can be used for the assessment of probabilistic seismic risk and earthquake scenarios. Information at the global, regional, and national levels is available through a public repository ( https://github.com/gem/global_exposure_model ), which will be used to maintain, update and improve the models.

Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms

Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs.

Multi-modal generative adversarial networks for synthesizing time-series structural impact responses

The process of validating newly-defined state observers can potentially require a significant amount of data gathered from instrumentation. However, collecting data for high-rate dynamic events (short time-scale impact and shock) can be very expensive. Additionally, experiments performed for collecting data can provide highly variable results while the high-energy impacts introduce damage into the structure being tested, consequently resulting in different results for subsequent tests. This paper proposes the use of Generative Adversarial Networks (GANs) to generate data that supplements the experimental datasets required for the validation of state observers. GANs are a class of deep learning models used for generating data statistically comparable to that on which it was trained. They are an ideal candidate for validation due to their consistency, speed, and portability during inference. In this work, data collected from an electronics package under shock is used to examine the generative ability of GANs. This paper proposes a conditional Wasserstein GAN (CWGAN) implementation for the production of synthetic high-rate dynamic vibrations, and introduces the conditional input to the critic at the layer towards the end as opposed to the first layers. Results suggest the generative model proposed is capable of producing data statistically similar to the provided training data. The generated data is compared to the training data, and the advantages and limitations of the model are explored. The model and its artifacts are provided as supplemental material to this article and shared through a public repository.

Gradient-Based Metrics for the Evaluation of Image Defogging

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository.

An Algorithm Predicting the Optimal Mechanical Response of Electronic Energy Difference.

The use of mechanical forces at the molecular level has been shown to be an interesting tool for modulating different chemical and physical molecular properties. The so-called covalent mechanochemistry deals with the application of precise mechanical forces that induce specific changes in the structure, stability, reactivity, and other physical properties. The use of this kind of force to modulate photophysical properties and photochemical reactivity has also been studied. Nevertheless, the general problem of mechanical modulation of the energy gap between two electronic states has been addressed only with the development of simple theoretical models. Here, we develop and implement an algorithm providing the Largest energy Gap variation with Minimal mechanical Force (LGMF) that allows the determination of the optimal mechanical forces tuning the electronic energy gap, as well as to identify the maximum mechanical response of a molecular system to the application of any mechanical stimulus. The algorithm has been implemented for diverse molecular systems showing different degrees of flexibility. The phyton code of the algorithm is available in a public repository.

Intuition - Knowledge over Management Science: Lessons from the past decades

The subject of intuition in management is interesting and has been discussed for decades. Repositories of scientific publications have in their collections hundreds of publications related to the use of intuition in management. It is necessary and reasonable to summarize the achievements of world science in this area. The best tool to do this will be bibliometric analyse. Bibliometric analysis is a popular method for exploring and analysing large volumes of scientific data but it`s application in business research is relatively new. A review of publications on this subject will indicate the state of this area of research. As part of the research, an analysis of the number of publications and the number of citations, and keywords statistics will be carried out. The database will be Web of Science. Zotero and VOSviever program will be the research tools. The review will be carried out following the methodology and will be divided into 5 stages, comprising: preliminary search, full search and literature acquisition, evaluation of collected documents, synthesis and analysis of results, and summary of results. The results will allow for assessing the research activity and the stage of development of the research in the field of intuition in management. A map of keywords related to the described issue will be created. What is the level of interest of scientists in this subject? Is it growing or going down? It will reliably summarise the current state of knowledge, providing information about gaps in the literature. It can also be a way to identify new research areas.

Tests on reinforced concrete U-shaped walls subjected to torsion and flexure

This data paper describes an experimental campaign on two reinforced concrete (RC) U-shaped walls, including details of the test units, materials, test setup, loading protocol, instrumentation, primary observations and response of each unit during testing, organization of the test data in the public open source research data repository (DOI: 10.14428/DVN/FDJ4EU), and examples of derived data. The two wall units represent the lower stories of a half-scale 6-story prototype core wall that was designed for medium-to-high ductility. The tests conducted aimed at assessing the flexural and torsional response and capacity of RC U-shaped walls. The first wall unit, UW1, was tested for axial-flexure and subjected to quasi-static reverse-cyclic bending about its weak axis until failure. Furthermore, UW1 was subjected to an additional overturning moment to increase the shear span. The second wall unit, UW2, was tested for axial-torsion and subjected to a quasi-static reverse-cyclic rotation about its vertical axis until failure. Both wall units were subjected to a constant axial load ratio of 5%. A range of conventional instrumentation was used to capture salient global and local measurements of the wall, including three-dimensional (3D) digital image correlation on multiple faces. High-definition fiber-optic sensors were also utilized to capture the entire strain profile of several longitudinal reinforcing bars in each wall unit.

B3X: a novel efficient algorithm for accurate automated auscultatory blood pressure estimation

Objective. The auscultatory technique is still considered the most accurate method for non-invasive blood pressure (NIBP) measurement, although its reliability depends on operator’s skills. Various methods for automated Korotkoff sounds analysis have been proposed for reliable estimation of systolic (SBP) and diastolic (DBP) blood pressures. To this aim, very complex methodologies have been presented, including some based on artificial intelligence (AI). This study proposes a relatively simple methodology, named B3X, to estimate SBP and DBP by processing Korotkoff sounds recordings acquired during an auscultatory NIBP measurement. Approach. The beat-by-beat change in morphology of adjacent Korotkoff sounds is evaluated via their cross-correlation. The time series of the beat-by-beat cross-correlation and its first derivative are analyzed to locate the timings of SBP and DBP values. Extensive tests were performed on a public database of 350 annotated measurements, and the performance was evaluated according to the BHS, AAMI/ANSI, and International Organization for Standardization (ISO) quality standards. Main results. The proposed approach achieved ‘A’ scores for SBP and DBP in the BHS grading system, and passed the quality tests of AAMI/ANSI and ISO standards. The B3X algorithm outperformed two well-established algorithms for oscillometric NIBP measurement in both SBP and DBP estimation. It also outperformed four AI-based algorithms in DBP estimation, while providing comparable performance for SBP, at the cost of a much lower computational burden. The full code of the B3X algorithm is provided in a public repository. Significance. The very good performances ensured by the proposed B3X algorithm, at a low computational cost and without the need for parameter training, support its direct implementation into clinical blood pressure (BP) monitoring devices. The results of this study pave the way for solving/overcoming the trade-off between the accuracy of the auscultatory technique and the objectivity of oscillatory measurements, by bringing an automated auscultatory BP measurement method in clinical practice.

Mixed-mode Ensemble Asteroseismology of Low-luminosity Kepler Red Giants

We present measurements of the dipole mode asymptotic period spacing (ΔΠ1), the coupling factor between the p- and g-modes (q), the g-mode phase offset (ϵ g ), and the mixed-mode frequency rotational splitting (δ ν rot) for 1074 low-luminosity red giants from the Kepler mission. Using oscillation mode frequencies extracted from each star, we apply Bayesian optimization to estimate ΔΠ1 from the power spectrum of the stretched-period spectrum and to perform the subsequent forward modeling of the mixed-mode frequencies. With our measurements, we show that the mode coupling factor q shows significant anticorrelation with both the stellar mass and metallicity, and can reveal highly metal-poor stars. We present the evolution of ϵ g up the lower giant branch up to before the luminosity bump, and find no significant trends in ϵ g or δ ν rot with the stellar mass and metallicity in our sample. Additionally, we identify six new red giants showing anomalous distortions in their g-mode pattern. Our data products, code, and results are provided in a public repository.

PLANT CHECK: POTATO LEAF DISEASE DETECTION USING CNN MODEL

Agriculture accepts a basic part by virtue of the quick improvement of the general population and extended interest in food in India. Hence, it is required to increase harvest yield. One serious cause of low collect yield is an infection brought about by microorganisms, infection, and organisms. Potatoes are a well-known vegetable to all of us. Potato cultivation has been very popular in India form the last few decades. But potato production is being hampered due to diseases like early blight and late blight which are increasing the cost of production. The objective of this research is to establish an efficient and expedited process for detecting diseases in potatoes, with the aim of boosting potato production and digitizing the existing system. Our primary aim is to diagnose potato diseases by utilizing a CNN algorithm to analyse leaf images. This study presents an automated system, based on image processing and machine learning, for the detection and classification of potato leaf diseases. Image processing emerges as the most effective approach for identifying and analysing these diseases. To conduct this analysis, our team divided more than 2000 leaf pictures of healthy and unhealthy potatoes, obtained from the Kaggle platform. We incorporated several preprepared models to accurately identify and classify healthy and diseased leaves. Through rigorous testing, the program demonstrated an impressive accuracy of 91.41%, using a 30% test data and 70% train data split. The dataset used in this study was sourced from the renowned public data repository, Kaggle, specifically the "Plant Village" dataset.

Abstract P386: Rat Remap Initiative At The Rat Genome Database (RGD): A Resource Of Integrated Transcriptomic And Epigenomic Data For Laboratory Rats

Complex traits including the genetic control of blood pressure have been associated with both coding and non-coding single nucleotide polymorphisms (SNPs) at more than a thousand loci across the human genome. Many of these haplotypes are found between and distant from genes, and their mechanisms leading to blood pressure variation are largely unknown. Attempts to compare and translate these non-coding mechanisms to animal model systems such as the laboratory rat are difficult, but even further complicated by the difficulty of accessing already existing large-scale genome-wide datasets including transcriptomic and epigenomic (e.g. chromatin accessibility, transcription factor binding, and DNA methylation) data that are currently distributed across a variety of public data repositories and supplementary datasets. These datasets are often annotated to previous and less complete reference genome assemblies, further hampering their broad use without time consuming reanalysis. Collectively, these challenges impede discovery and leveraging of these datasets across the broader research community toward understanding mechanisms controlling complex traits such as blood pressure. To address these challenges, the Rat ReMap Initiative seeks to establish a framework for a publicly accessible resource for rat genomic, transcriptomic and epigenomic data to housed within RGD, with analysis updated to the current reference genome assembly. Users will be able to explore and find these datasets, link to the original raw data, and access analyzed summary data files. Remapped datasets will be linked to existing genomic features, strain, and genetic variation annotations to link with disease, pathways, phenotypes, and models portals already annotated and maintained in the Rat Genome Database (RGD). Here we report our initial efforts to standardize metadata and remap a targeted set of publicly available rat transcriptome, chromatin accessibility and transcription factor binding datasets, some across multiple ages and both sexes, onto the newest rat reference assembly (mRatBN7.2), and to integrate them into an updated genome browser (JBrowse2) and into a comparative genome map browser (VCMap) at RGD.

Tackling the lack of diversity in cancer research.

Despite the clear benefit of studying biological samples from diverse genetic backgrounds and geographical locations, our current knowledge of disease is mostly derived from the study of European-descent individuals. In the cancer field, this is reflected in the poor representation of African and Amerindian/Latino samples in most large public data repositories. This lack of diversity is due to several reasons, but here we focus on (1) the lack of support for studies on non-European populations that are performed in low- and middle-income countries (LMICs), and (2) unequal partnerships between scientists in LMICs and those in high-income countries. We argue that expanding access to research funding, increasing the participation of underrepresented scientists in editorial boards and international conferences, facilitating the publication of studies conducted in these countries, and properly acknowledging LMIC researchers' contributions in publications and grant applications will promote equity for scientists working in LMICs. We envisage that this will translate to more impactful research in these countries, which will include more samples from diverse populations. For the cancer field, this will broaden our understanding of pathomechanisms and may help to improve the treatment of patients from all backgrounds.

Improving FAIRness of eDNA and Metabarcoding Data: Standards and tools for European Nucleotide Archive data deposition

The advancements in sequencing technologies have promoted the generation of molecular data for cataloguing and describing biodiversity. The analysis of environmental DNA (eDNA) through the application of metabarcoding techniques enables comprehensive descriptions of communities and their function, being fundamental for understanding and preserving biodiversity. Metabarcoding is becoming widely used and standard methods are being generated for a growing range of applications with high scalability. The generated data can be made available in its unprocessed form, as raw data (the sequenced reads) or as interpreted data, including sets of sequences derived after bioinformatics processing (Amplicon Sequence Variants (ASVs) or Operational Taxonomic Units (OTUs)) and occurrence tables (tables that describe the occurrences and abundances of species or OTUs/ASVs). However, for this data to be Findable, Accessible, Interoperable and Reusable (FAIR), and therefore fully available for meaningful interpretation, it needs to be deposited in public repositories together with enriched sample metadata, protocols and analysis workflows (ten Hoopen et al. 2017). Metabarcoding raw data and associated sample metadata is often stored and made available through the International Nucleotide Sequence Database Collaboration (INSDC) archives (Arita et al. 2020), of which the European Nucleotide Archive (ENA, Burgin et al. 2022) is its European database, but it is often deposited with minimal information, which hinders data reusability. Within the scope of the Horizon 2020 project, Biodiversity Community Integrated Knowledge Library (BiCIKL), which is building a community of interconnected data for biodiversity research (Penev et al. 2022), we are working towards improving the standards for molecular ecology data sharing, developing tools to facilitate data deposition and retrieval, and linking between data types. Here we will present the ENA data model, showcasing how metabarcoding data can be shared, while providing enriched metadata, and how this data is linked with existing data in other research infrastructures in the biodiversity domain, such as the Global Biodiversity Information Facility (GBIF), where data is deposited following the guidelines published in Abarenkov et al. (2023). We will also present the results of our recent discussions on standards for this data type and discuss future plans towards continuing to improve data sharing and interoperability for molecular ecology.

FlowSim: Near duplicate detection for flow cytometry data.

The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values. flowSim clustering compared to consensus manual clustering on a dataset composed of 160 images of bivariate FCM data had a mean Adjusted Rand Index of 0.90, demonstrating its efficiency in identifying similar patterns. flowSim selectively discarded near duplicate files in datasets constructed with known redundancy, and removed 92.6% of FCM images in a dataset of over 500,000 drawn from public repositories.

Papers with code or without code? Impact of GitHub repository usability on the diffusion of machine learning research

Open Science initiatives prompt machine learning (ML) researchers and experts to share source codes - "scientific artifacts" - alongside research papers via public repositories such as GitHub. Here we analyze the extent to which 1) the availability of GitHub repositories influences paper citation and 2) the popularity trend of ML frameworks (e.g., PyTorch and TensorFlow) affects article citation rates. To accomplish this, we connect ML research publications indexed by Papers with Code (PwC) to Microsoft Academic Graph (MAG) and collect repository-level metadata using the GitHub API. Applying nearest-neighbor matching and econometric considerations, we estimate that papers enjoy approximately 20% advantages in monthly citation rates after the creation of the first GitHub repositories, accounting for paper-level fixed effects and ages. We also find that the temporal popularity trends for frameworks used in the first associated repositories could influence the monthly citation rate for papers. The results highlight the importance of technological artifacts and infrastructure latent to the diffusion of research.

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

GeoPick: Georeferencing Made Easy

GeoPick is a new web application aimed at providing a simple yet powerful georeferencing tool to the natural history collections community (Fig. 1). Its conceptual foundation is based on the Georeferencing Best Practices by Chapman and Wieczorek (2020), whose guidelines it intends to implement. GeoPick also provides a close and direct relation between the tool’s output and the Darwin Core standard (Wieczorek et al. 2012). In the past two decades, institutions across the world have devoted significant resources to digitise their collections and bring them closer to their final users (Nelson and Ellis 2018), i.e., the research, conservation and education communities, and to the general public. Georeferencing is an important part of the digitisation process (Nelson et al. 2012). It provides the link between the preserved specimen and the natural habitat where it lived (Bloom et al. 2017). However, there is a gap between the data that have already been digitised and made accessible through public repositories such as the Global Biodiversity Information Facility (GBIF) and the expected fully georeferenced information, including crucial information on coordinate uncertainty and precision, which is needed to conduct rigorous studies based upon digitised specimens (Marcer et al. 2022). In order to determine the reasons behind this mismatch between optimal and actual georeferenced data, we conducted a global survey on current georeferencing practices (Marcer et al. 2021a), which was used as the basis for discussion in a workshop organised by the MOBILISE Cost Action that took place in Warsaw (Poland) in 2020 (Marcer et al. 2020, Marcer et al. 2021a). Among other recommendations that came out of the workshop was the need to develop more user-friendly tools that help georeferencers follow standards and guidelines in a more effective way. According to the survey, only 16 percent of respondents used a specific georeferencing tool such as GeoLocate (Rios 2019), while the rest only used general tools (Google Maps/Earth was reported by 47% of the respondents). Moreover, about 75% of the respondents reported only following in-house ad hoc protocols or none at all. This discouraging situation and the need for a tool that is easy to use, yet effective, was discussed informally with attendees at the 2022 Biodiversity Information Standards (TDWG) and Society for the Preservation of Natural History Collections (SPNHC) conferences. The current version of GeoPick is the first step towards a tool that is powerful, accessible and simple enough to help georeferencers follow standards and best practices in order to improve the data that are finally made accessible through repositories such as GBIF. In this first version, GeoPick implements the point-radius (Wieczorek et al. 2004) and shape (Chapman and Wieczorek 2020) georeferencing methods. It calculates the centroid or corrected centre when necessary, the associated coordinate uncertainty, the spatial fits of the point-radius and the footprint, and provides the digitised geometry as Well-Known Text (WKT). The tool offers a map navigator with reference cartography and editing tools to digitise points, lines or polygons representing the locality description of a given location. Data are finally exported via the clipboard in Darwin Core standard format. The tool also offers search functionality for geographic features from OpenStreetMap data through the Nominatim API. The user can search and import a geometry provided by Nominatim and use it as the basis of a georeference. Technical implementation: HTML and JavaScript browser front end that accesses a private API developed in R (R Core Team 2022). Source code availabe at GitHub. License: GeoPick is open source with a AGPLv3 license. Public URL: https://geopick.gbif.org (available starting October 2023)

Phylobone: a comprehensive database of bone extracellular matrix proteins in human and model organisms

The bone extracellular matrix (ECM) contains minerals deposited on highly crosslinked collagen fibrils and hundreds of non-collagenous proteins. Some of these proteins are key to the regulation of bone formation and regeneration via signaling pathways, and play important regulatory and structural roles. However, the complete list of bone extracellular matrix proteins, their roles, and the extent of individual and cross-species variations have not been fully captured in both humans and model organisms. Here, we introduce the most comprehensive resource of bone extracellular matrix (ECM) proteins that can be used in research fields such as bone regeneration, osteoporosis, and mechanobiology. The Phylobone database (available at https://phylobone.com) includes 255 proteins potentially expressed in the bone extracellular matrix (ECM) of humans and 30 species of vertebrates. A bioinformatics pipeline was used to identify the evolutionary relationships of bone ECM proteins. The analysis facilitated the identification of potential model organisms to study the molecular mechanisms of bone regeneration. A network analysis showed high connectivity of bone ECM proteins. A total of 214 functional protein domains were identified, including collagen and the domains involved in bone formation and resorption. Information from public drug repositories was used to identify potential repurposing of existing drugs. The Phylobone database provides a platform to study bone regeneration and osteoporosis in light of (biological) evolution, and will substantially contribute to the identification of molecular mechanisms and drug targets.