dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022) ; (3) coordinating ELIXIR Node participation in Horizon Europe project proposals -The Biodiversity Community Integrated Knowledge Library (BiCIKL) and Biodiversity Genomics Europe (BGE); and (4) beginning to establish connections with key external partners/projects in the biodiversity domain (such as those listed in Table  1 ); leading to the formal recognition in 2022 as an ELIXIR Community  (Waterhouse et al. , 2023). Tackling the biodiversity crisis at a general level is not going to be resolved through a single action, but instead requires a complex set of interacting actions that are co-dependent but usually funded separately. ELIXIR can assume a key leading role in a subset of those actions, focused on data management and the molecular sciences, where even at the level of ELIXIR, there are a multitude of funded projects at a transnational, national, and local level.
These themes come together in a complex network of interacting projects that have distinct but related aims, usually focused on establishing communities and connections and/or building new technical solutions to help with data access, storage, or analysis. ELIXIR can serve a critical function here, as a fundamental aspect of its mission is to make connections and coordinate across complex activities. (Black et al. , 2022) ; and an extended coverage of biodiversity research concepts could be achieved via engagement with the Biodiversity Community. • Specifically for the Compute Platform: User accessible compute, potentially controlled user access via Authentication and Authorisation Infrastructure (AAI). • Community data-management support, and integration with ELIXIR Core and Deposition Data resources.
The European Nucleotide Archive (ENA) is a critical data deposition resource for biodiversity genomics data. A concrete example of metadata management workflow is that developed between biodiversity scientists, the Data Platform, and the Biodiversity Community Integrated Knowledge Library (BiCIKL) project  (Penev et al. , 2021 (Penev et al. , , 2022)) : a metadata management workflow employs the PlutoF tool for biodiversity data and metadata management  (Abarenkov et al. , 2010) , and the ELIXIR Data Platform services. • Networks of tool/infrastructure users and developers to augment the Training Platform offerings (e. g. , with specific courses covering aspects such as: genome annotation, meta-data brokering, etc. ) and more complete learning paths, covering entire workflows (e. g. , from sequencing to annotation, possibly covered via Galaxy).
• A growing necessity in the biodiversity field towards connected data, as championed by the Interoperability Platform, concretely touching on resources like: RO-Crate and link to specimens, RDMkit, FAIRsharing, Bioschemas and the FAIRcookbook. The ELIXIR Biodiversity Community aims to bring together researchers producing the data, in all their varied forms, with informaticians developing interoperability solutions, to help overcome the challenges of data heterogeneity in the field. Regarding links between the ELIXIR Biodiversity Community and other ELIXIR Communities, these are already foreseen, and a number of synergies have been clearly identified. Some examples can be found in Table  2. A ELIXIR provides services in seven scientific domains including "Evolution and phylogeny" and "Genes and genomes" (https://elixir-europe.
org/services) that link the activities of the ELIXIR community to the wider landscape of life-science research infrastructures (RIs) and international projects. As RIs mature and FAIRness has become the standard to achieve interoperability between RIs, it is opportune to outline the global network of interrelated projects and infrastructures, in which ELIXIR operates to maximise synergy and to avoid redundancy. The relationships between different aspects of biodiversity data are well captured by the biodiversity knowledge graph of Roderic Page (Figure  1 ). The key activities of ELIXIR are captured by the molecular domain; the biodiversity knowledge graph clearly indicates how molecular data are related to the wider spectrum of biodiversity data that are targeted by other RIs and projects.
The ELIXIR Biodiversity Community benefits from connections to RIs and projects in the biodiversity domain, an overview of which can build on the landscape analyses of the ESFRI roadmaps of ESFRI 2018 (ESFRI, 2018) and 2021 (ESFRI, 2021), the partners of the Alliance for Biodiversity Knowledge, and the research infrastructure contact zones analysis between 10 biodiversity infrastructures, including ELIXIR  (Smith et al. , 2022). Additional to the data types considered by Page (Figure  1 ), the contact zones analysis considers 'observations' and 'collections', or groups of specimens, as elements of the biodiversity data domain. This recognition of the variety of types of biodiversity data and the importance of integration has been key to the establishment of many  In addition to the above examples of biodiversity projects and infrastructures that make up the global network of ongoing efforts in the domain, and in the context of ELIXIR's participation and contribution to the network, the following two examples highlight ongoing activities in the field of biodiversity and in the context of the European research sphere.
Example: Biodiversity Community Integrated Knowledge Library (BiCIKL) Several ELIXIR Nodes are involved in European projects with a focus on biodiversity. The BiCIKL project is building the Biodiversity Knowledge Hub (BKH) -a single knowledge portal to interlinked machine-readable FAIR data -using unique stable identifiers on specimens, sequences, taxonomy and publications  (Penev et al. , 2021 (Penev et al. , , 2022)). A set of core global biodiversity databases (GBIF, ENA, PlutoF, Plazi, DiSSCo, OpenBioDiv, ToL, etc. ) are contributing with the aim to develop services to augment the interlinking of biodiversity contents, starting with biotic interactions. The project is also financing competitive implementation studies to develop transnational resources.
5. To foster knowledge transfer in biodiversity data management and analysis by: supporting community-driven skills sharing focused on understanding how to benefit from the use of available standards and best practices; connecting developers of tools/workflows/databases with user communities through training that responds to changing technologies and associated services; and exposing collections of training materials, for example through TeSS, the Galaxy Training Network, and RDMkit. To complement these guiding goals, the ELIXIR Biodiversity Community proposes a roadmap towards achieving our long-term objectives. Table  3  shows five long-term objectives for the ELIXIR Biodiversity Community to address, Table  3. The ELIXIR Biodiversity Community long-term objectives.
Objectives Example approaches Identify and support key stakeholders in the biodiversity domain • Support efforts to harmonise data management activities within large-scale projects and transcending initiatives to result in high-quality, interoperable data and metadata • Build routes for user communities to access and add to the knowledge graph (curation) of growing resources e. g. , trait measurements, observations beyond geolocations etc. • Include primary production sectors (industry etc. ) affecting biodiversity in the collaborative processes aiming to identify data-informed win-win solutions centred on engaging with stakeholders, aligning infrastructures, contributing to policy, supporting production, and enhancing ELIXIR's portfolio in the biodiversity domain. The current focus is on the informatics, databases, and tools more than on the biological questions, so as the Community grows, it will be important to widen the diversity of its membership to ensure that the technical developments will serve the needs of biodiversity researchers.
As noted in the article, "ELIXIR can serve a critical function here, as a fundamental aspect of its mission is to make connections and coordinate across complex activities. " And "the biodiversity community needs to proactively seek common solutions (without unnecessary duplication of effort) that enable molecular technologies to advance biodiversity research. I would underscore the importance of limiting duplication of effort. Much of the history of biodiversity conservation in marked by unnecessary competition, stove-piping, and duplicated effort among those involved. And while ELIXIR's "current focus is on the informatics, databases, and tools more than on the biological questions,. as the Community grows, it will be important to widen the diversity of its membership to ensure that the technical developments will serve the needs of biodiversity researchers," I would encourage this community to begin early to seek out and identify feedback loops from the full spectrum of user communities.
The authors acknowledge the following grant support:  Swiss National Science Foundation (SNSF)  grant number  PP00P3_202669  and  Swiss State Secretariat for Education, Research and Innovation (SERI)  contract numbers  22. 00173  and  24. 00054  to RMW. Institut Français de Bioinformatique (IFB)  grant number  ANR-11-INSB-0013  and  French National Infrastructure of resources for Research   in Agriculture (SOERE RARe )  to AFAB;  Green Fund ,  Hellenic Ministry of Environment and Energy ,  Funding Program "Natural Environment & Innovative Actions", Priority Axis 3 "Research and Implementation " Grant number  83078  to ACP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Reviewer Report 13 13 February 2024 https://doi. org/10. 5256/f1000research. 146735. r218316 © 2024 Comer P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Patrick Comer NatureServe, Arlington, Virginia, USA Peer Review: The ELIXIR Biodiversity Community: Understanding short-and long-term changes in biodiversity -RM Waterhouse et al This article provides a concise overview of the ELIXIR Biodiversity Community which can serve as a hub for the molecular sciences and bioinformatics as it applies to biodiversity science. ELIXIR's overarching mission has been stated to "support the management of public research data, integrate and coordinate life science resources, and foster the development of innovative services and technical solutions in Europe" (Harrow et al.
Sample Description and Microstructural Characterisation The block of limestone used for this study was collected from a quarry in Guzd village about 20 km west of Baku (Figure  1b ). The limestone is a porous, shelly o limestone (Figure  3 ). The ooids and shells within the limestone (typically <1 mm surrounded by sparry calcite cement (Figure  3 ). The ooids are typically round in s whereas the shells are characterised by low aspect ratios (minor grain axis divided b major grain axis) (Figure  3 ). To characterise the microstructural heterogeneity of the studied limeston provide here a grain size and shape analysis. To do so, we manually drew around a grains within a selected optical microscope image using open-source vector gra program Inkscape. We then analysed these grains using open-source image an software ImageJ.
Using ImageJ, we determined the equivalent grain diameter an grain aspect ratio of each grain (n = 623). The equivalent grain diameter, , was calcu using  = 3/2( ) , where  is the average Feret diameter. The aspect ratio of To characterise the microstructural heterogeneity of the studied limestone, we provide here a grain size and shape analysis. To do so, we manually drew around all the grains within a selected optical microscope image using open-source vector graphics program Inkscape. We then analysed these grains using open-source image analysis software ImageJ. Using ImageJ, we determined the equivalent grain diameter and the grain aspect ratio of each grain (n = 623). The equivalent grain diameter, d, was calculated using d = 3/2(d F ), where d F is the average Feret diameter. The aspect ratio of each grain was calculated as the minor grain axis divided by the major grain axis (an aspect ratio of unity therefore represents a perfect circle).
Permeability Measurements Permeability was measured using a benchtop gas (nitrogen) permeameter (see schematics in  [20, 21] ) at ambient temperature and under a confining pressure of 1 MPa using either the steady-state method (for high-permeability samples) or the pulse-decay method (for low-permeability samples). For the steady-state experiments, steady-state volumetric flow rates were measured (using a gas flowmeter) for six different pore pressure differentials (measured using a pressure transducer). Pulse-decay measurements were performed by monitoring the decay of a pressure differential over time (measured using a pressure transducer). These data were used to calculate permeability using Darcy's law. When necessary, the data were corrected using the Klinkenberg or Forchheimer corrections.
Uniaxial Compressive Strength (UCS) and Young's Modulus Uniaxial compressive strength was measured using a uniaxial load frame (see schematic in  Heap et al. [23] ). Dry samples were deformed at ambient temperature at an axial strain rate of 10 -5 s -1 until macroscopic failure. Axial displacement and axial force were measured during the experiment using a linear variable differential transducer and a load cell, respectively, and were converted to axial strain and axial stress using the sample dimensions. Static Young's modulus was calculated using the stress-strain data from the elastic portion of the experiments (as per the method outlined in Heap et al. [25] ).
The imagery employed are not free of charge, but the cost is order of magnitudes lower than conventional sub-meter imagery  15. We developed a deep learning-based method that can be trained with simple point labels, which can be rapidly optimized for regional use  17, 18, [21] [22] [23]. The database does not only provide a complete inventory of mature agroforestry trees, but also offers the opportunity to locate and quantify trees that have disappeared. The database includes detection con dence values for each year 2018-2022. If a tree that has been detected with a high con dence (above a given threshold) is not detected anymore over several consecutive years, there is a high probability that the tree has disappeared. By combining the con dence values of each year, a change con dence can be derived, quantifying the uncertainty of wether a tree was has disappeared or remained.
In this study we refrained from making statements on low or medium con dence changes in order to take a conservative approach to the reporting of losses, but the dataset will be open for various applications. The database can be updated at an annual basis and scaled to global scale, potentially being a tool to support scientists, agroforestry practitioners, land managers, and policymakers  10. Potential applications beyond inventorying are the monitoring of illegal logging of trees and the monitoring of survival rates of restoration areas. Second, our study reveals a concerning trajectory, documenting a considerable depletion of India's large agroforestry trees over the past decade. This nding is particularly unsettling given the current emphasis on agroforestry as an essential natural climate solution, playing a crucial role in both climate change adaptation and mitigation strategies, as well as for livelihoods and biodiversity  [24] [25] [26] [27] [28].
Since there is no reliable metadata on cloud cover in RapidEye images, we calculated the standard deviation for each downloaded scene, and disregarded all scenes where the blue band had a very high standard deviation, which is typically the case in cloudy images. This did not guarantee fully cloud-free images, but, together with the blur kernel, removed most of the contaminated scenes. The entire framework was automated and the lling of a 1x1 degree tile required about 2-3 hours using a university connection, but can be performed in a parallelized way, so the downloading, ltering and processing of one year requires less than one week for all India. All processing was done locally, and the raw data for one year is about 2. 5 terrabyte. As a nal step, we applied a Contrast Limited Adaptive Histogram Equalization (CLAHE) to each mosaic and normalized the bands to values between 0-255.
Training data We manually labeled trees with point labels at the tree crown center, using separate models for PlanetScope (about 130,000 labels) and RapidEye (about 100,000 labels), including labels from all years. For each labeled tree, we used high resolution images in Google Earth and Bing to verify if the trees existed. A large number of sub-meter images around 2010 were available from Bing maps and were used to verify labels from RapidEye, while Google Earth images were from recent years and used to verify labels from PlanetScope images. The labels were generated as in iterative process. In the rst round, labels were generated over a variety of landscapes and image conditions (that is different viewing angles, sharpness levels, etc), being spatially well distributed across India. Then a rst model was trained and trees were predicted over the study area.
7 m for RapidEye). We trained the models over 2500 epochs, with a learning rate of 1e-5 until epoch 2000, after which we used a linearly decreasing learning rate. The training process was monitored using 20% of the training data as validation data. We trained 5 models with different random splits of the training data (but always used 80% for training and 20% for validation). The F1 scores on the heatmaps for all models ranged between 0. 44-0. 47 for a close radius of 5 m, and 0. 67-0. 69 for a wider radius of 50 m, which is close to the values from ref. 17  , although they used aerial images of higher spatial resolution. We then predicted on the mosaics using an ensemble over the 5 models and averaged the results of the heatmaps. The local maxima of the ensemble heatmap were converted to a point le, re ecting the centers of tree crowns.
8). Note that trees with a crown size >50 m 2 or 100 m 2 represent only a small fraction of the woody populations, with a proportion of about 20% and 4%, respectively (Extended Data Table  1 ). We sampled trees from the heatmap predictions for the same area from both PlanetScope (n = 2. 1 million) and RapidEye (n = 1. 2 million) for 2019 (where RapidEye was still operational). We used the tree crown segmentation to determine the tree crown sizes of the point predictions from both PlanetScope and RapidEye, by overlaying the tree crown segments with the point predictions and associating the crown area as an attribute to the points. We found that the average tree crown size of the samples was 55 m 2 from PlanetScope and 62 m 2 from RapidEye. Trees detected with a con dence >0. 8 had an average crown size of 96 m 2 for RapidEye.
To account for this, we assumed that high quality images in general generate higher con dence predictions compared to lower quality images, so we calculated the average detection con dence of all trees within 1x1 km cells as a measure of image quality for each particular cell, which also accounts for quality variations within images. We then calculated a linear slope through the 1x1 km con dence grids, with time as the independent variable. A strong negative slope (< -0. 05) implies that the average con dence in 2020-2022 was considerably lower than in 2018-2019, so we agged this area as uncertain, and losses were not included in our reported numbers. The con dence slope map represents an additional layer (to the change con dence) that quanti es uncertainty that we provide with the database.
Code availability. The tree detection framework is available from https://arxiv. org/abs/2208. 10607. The crown segmentation framework is available from https://doi. org/10. 5281/zenodo. 3978185. A framework including both segmentation and counting can also be found at https://github. com/sizhuoli/TreeCountSegHeight. The PlanetScope mosaic generation is available from https://doi. org/10. 5281/zenodo. 7764360. Data availability. The data will made available upon publication of the manuscript.
Predictions with high confidence are added to the set of training labels, and the process is repeated. Wrapper methods also include approaches based on boosting  [15]  or co-training  [16]. Unsupervised preprocessing is when computations are performed on unlabeled data to make training on labeled data more efficient. Feature extraction can be done on unlabeled data, by transforming the original dataset to a new one with fewer features, for example with AutoEncoders  [17, 18]. Clustering can also be used to represent the unlabeled data in groups before classification  [19]. Preprocessing can also refer to pre-training  [20, 21] , meaning that weights of the model are first learned on unsupervised data, and they only need to be adjusted (fine-tuned) with labeled data to fit the classification problem better.
Results A3SOM is implemented using the TensorFlow  [61]  Python API, Keras  [62] , and the implementation of a Keras layer for SOM given in  [63]. We present results for each task performed by A3SOM. For standard semi-supervised classification, we illustrate A3SOM's performance compared to other classifiers on several datasets (Section 4. 1). For the abstained classification task, we show using an artificial dataset that abstaining from prediction can improve accuracy and reduce the number of classification errors (Section 4. 2). Finally, we illustrate the usefulness of A3SOM in a case study relating to the diagnosis of breast cancer (Section 4. 3).
Semi-supervised classification evaluation In this section we compare A3SOM's semi-supervised classification results to other methods on several datasets. 4. 1. 1 Datasets. We use a total of six datasets in the benchmark. The first is an artificial dataset we generated to show the specificities of our method. The others are five public datasets. The artificial dataset is composed of 6 classes: three defined by Gaussian clusters (labeled 0, 1 and 2) and three with uniform distributions (labeled 3, 4 and 5), as shown in Fig 5. The classes are generated in order to have different degrees of ambiguity. Classes 3, 4 and 5 have a high ambiguity in their overlapping areas; class 5 overlaps with class 2; classes 0 and 3 are close but still separable, and class 1 is well separated from others. Dataset construction details are given in supplementary file A, Section 1.
The real-world datasets are extracted from the OpenML database  [64] : • Cardiotocography: measurements data on cardiotocograms  [65]. • Ionosphere: radar data  [66]. • Iris: different types of flowers  [67]. • MNIST: handwritten digits  [68]. • WDBC: breast lump description  [69]. All datasets are all normalized between 0 and 1 to reduce measurement bias, and instances with missing values are removed. A summary of the six datasets used is given in Table  1. 4. 1. 2 Benchmark methods. We compare A3SOM to other semi-supervised methods, which we can divide into three categories: implemented baselines, and existing semi-supervised approaches not SOM-based (SSL approaches) or SOM-based (SOM approaches). 4. 1. 2. 1. Implemented baselines. We implemented three combinations of self-training  [13] , which is a wrapper method, with popular classifiers: Support-Vector Machine (SVM)  [70] , Random Forest (RF)  [71] , and Multi-Layer Perceptron (MLP)  [72].