dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrode Localization. For the purpose of electrode localization in the iEEG recordings, we developed an image processing pipeline which involves electrode contact localization, brain tissue segmentation and atlas fitting. Semi-automatic contact localization was performed in 3D Slicer using the SEEG Assistant module  (Narizzano et al. , 2017). The entry and target points of each electrode were manually defined on the post-operative CT image. The entry/target labels were provided to the SEEGA algorithm, which automatically segmented the electrode contacts. To obtain brain location information for each contact, brain tissue segmentation and atlas fitting was carried out. To enable the use of anatomical priors during tissue segmentation, the pre-operative T1w MRI was non-linearly registered to the MNI152 2009c Nonlinear Symmetric template (https://www.
bic. mni. mcgill. ca/ServicesAtlases/ICBM152NLin2009) using NiftyReg  (Modat et al. , 2010 ). An anatomical mask was generated by applying the inverse transform to the T1w image using the antsApplyTransforms algorithm from Advanced Normalization Tools 2. 2. 0 (ANTS; http://stnava. github. io/ANTs). Segmentation of gray matter, white matter, and cerebrospinal fluid was performed using the Atropos algorithm from ANTS  (Avants et al. , 2011b) , which implements k-means classification (k=3). The resulting posteriors were merged into a 4D volume using the fslmerge algorithm from FMRIB Software Library v6. 0 (FSL; https://fsl. fmrib. ox. ac. uk/fsl/fslwiki). The CerebrA atlas  (Manera et al. , 2020)  was used to obtain anatomical labels for each electrode contact. Normalization to template space (MNI152NLin2009cAsym) was performed using the non-linear SyN  (Avants et al.
, 2011a)  symmetric diffeomorphic image registration algorithm from ANTS, using both the brain masks of the pre-operative T1w and template space. Using the inverse of the non-linear transform, the CerebraA atlas labels were warped to the pre-operative T1w MRI space. The atlas labels were then dilated using the fslmaths algorithm from FSL. The final T1w brain tissue/atlas segmentation was mapped to the contacts to provide location information for each contact (tissue probability and brain anatomical region). This custom processing pipeline has been made available on GitHub (https://github. com/akhanf/clinical-atlasreg).
The development of solutions to manage or mitigate climate change impacts is very challenging, given the complexity and dynamicity of the socioenvironmental and socio-ecological systems that have to be modeled and analyzed, and the need to include qualitative variables that are not easily quantifiable. The existence of qualitative, interoperable and well-interlinked data is considered a requirement rather than a desire in order to support this objective, since scientists from different disciplines will have no option but to collaborate and co-design solutions, overcoming barriers related to the semantic misalignment of the plethora of available data, the existence of multiple data silos that cannot be easily and jointly processed, and the lack of data quality in many of the produced datasets.
In the current work, we present the SustainGraph, as a Knowledge Graph that is developed to track information related to the progress towards the achievement of targets defined in the United Nations Sustainable Development Goals (SDGs) at national and regional levels. The SustainGraph aims to act as a unified source of knowledge around information related to the SDGs, by taking advantage of the power provided by the development of graph databases and the exploitation of Machine Learning (ML) techniques for data population, knowledge production and analysis. The main concepts represented in the SustainGraph are detailed, while indicative usage scenarios are provided. A set of opportunities to take advantage of the SustainGraph and open research areas are identified and presented.
, 2021) , Nationally Determined Contributions monitoring (United Nations Climate Action, 2022)), centered mainly around the need to monitor and track the values of indicators to assess the progress made towards the achievement of the SDG targets at national and regional levels. Given that these data are collected by various organizations worldwide, semantic consistency and data interoperability among them cannot be considered as granted. Furthermore, such data are made available in many cases as data silos, while specialized software or Application Programming Interfaces (APIs) may be required for getting access to them. Lack of data quality is also a barrier, since data processing (e. g. , removal of outliers, tackling of diverse assumptions during data production, use of different semantics for data description) is required in most cases to manage the transformation of data to formats and structure that can be considered homogeneous.
Thus, the proper management of the wealth of collected information is not straightforward. There is a need for information models and information management techniques able to capture the volatility of the data, manage semantic misalignment of the denoted concepts, and facilitate the identification of hidden patterns and relationships among them. In this way, a solid, open and interoperable data infrastructure can be made available, enabling the development of innovative solutions to produce systemic changes and make economies socially, economically and environmentally sustainable. Under this perspective, we present the SustainGraph as a Knowledge Graph (KG) that has been conceptualized and developed to track the progress towards the SDG targets, the evolution of the defined indicators across time and their interconnectedness with policies and targets defined at European Union (EU) and at national levels.
g. , dynamicity in the relationships across time in a complex system)  (Hogan et al. , 2021). A systemic nexus approach has been considered for supporting the data population processes of the KG, while taking advantage of participatory system mapping processes  (Matti et al. , 2020; Midgley and Lindhult, 2021). By the term systemic nexus, we refer to the interconnection of resource management concepts, considering resources such as energy, water, food, land and climate. In the context of the SDGs, a nexus approach can facilitate the advancement of multiple SDGs simultaneously, while reducing the risk that contributions to one SDG undermine progress on another  (van Zanten and van Tulder, 2021). To take advantage of the wealth of available data, openness and interoperability of the SustainGraph with existing databases and Application Programming Interfaces (APIs) is promoted to automate -as much as possible-the supported data population processes.
Over the SustainGraph, socioenvironmental and socio-ecological systems participatory modeling and analysis processes can take place, aligned with the main mechanics of a Systems Innovation Approach  (Matti et al. , 2020; Midgley and Lindhult, 2021). Specifically, the effective fusion of the collected data and their transformation to systematized nexus-coherent knowledge, can lead to novel insights  (Laspidou et al. , 2020) , significant improvement of the participatory processes  (Matti et al. , 2020)  and the development of collective environmental intelligence  (Zafeiropoulos et al. , 2021)  among the engaged stakeholders and communities. In short, it can be claimed that the main contribution of this work is twofold. On one hand, we provide the conceptualization and semantic description of the SustainGraph that, as far as we know, is the first KG that considers in a holistic way the tracking of the progress towards the SDG targets and the evolution of indicators at national and regional levels, along with their relationship with specified policies and the implementation of case studies across Europe.
This conceptualization is considered as the basis for the systemic representation of knowledge related to the SDGs, enabling the collection and homogeneous representation of data along with their semantics and overcoming the aforementioned data management barriers. On the second hand, by considering the mechanisms specified in a Systems Innovation Approach  (Matti et al. , 2020; Midgley and Lindhult, 2021) , we detail the implementation of the SustainGraph and the set of data population mechanisms from a plethora of open data sources and data providers. Data population to the KG and data analysis over the KG are assisted through the exploitation of Machine Learning (ML) techniques. In this way, participatory modeling and analysis processes can be designed and implemented, taking advantage of the semantic alignment of the represented terms and the knowledge produced through the analysis of the information that is made available in the SustainGraph.
Knowledge graphs for information management Knowledge Graphs (KGs) are emerging, since they are considered suitable to manage challenges that have arisen in modern data practices. The main challenge has to do with the existence of silos of data or dedicated software and Application Programming Interfaces (APIs) for managing such data  (Sequeda and Lassila, 2021). Industry-specific data representation schemas are defined and adopted that -in many cases-may differ, even for the management of the same types of data. In parallel, dedicated software and APIs are being developed for data management in specific sectors, where the data semantics are hidden from the end users and are tackled by the internal software components. This make the software usable only for the purpose that has been initially designed and hinders its adoption, re-usability and interoperability with other data management tools  (Sequeda and Lassila, 2021).
KGs are considered suitable for bridging data silos, by interlinking the concepts represented in the graphs with welldefined semantics (see Figure  3 ). In this way, the interconnected datasets in the KG can be enriched with meaning, misalignment of terminologies of the same concepts under different data schemas can be tackled, while relationships among concepts can be made explicit. Thus, the main motivation for the development of a KG is the usage of graphs to represent data -that can be interconnected and enriched with meaning-to explicitly represent knowledge  (Noy et al. , 2019; Hogan et al. , 2021). Data volatility is managed, since relationships among nodes in a KG can be dynamic, making them suitable for representation of complex and dynamic systems (e. g. , socioenvironmental systems  (Zafeiropoulos et al.
et al. , 2020). Through reasoning, KG completion and evolution can be supported via the identification and prediction of new relationships among entities  (Chen Z. et al. , 2020; Issa et al. , 2021). As already mentioned, KGs can also act as an enabler for participatory analysis of dynamic and complex systems by interdisciplinary scientists. A data scientist is able to take advantage of the interlinked data in the KG to identify transformative patterns and extract new knowledge and insights. The existence of semantically aware and up-to-date data within a graph database enables the codesign of data management and analysis processes that can be integrated within dynamic modeling systems. The role of Artificial Intelligence (AI) is highlighted since Machine Learning (ML) pipelines can be developed for supporting both data population and data analysis in the KG.
However, the LPG model does not support a formal language representation that can be used for automated knowledge reasoning. To achieve this, the semantics of the data have to be introduced and managed, as it happens in the case of development of an ontology. To properly detail the semantic information associated with each node and relationship, a SustainGraph ontology has been made available  (Mandilara et al. , 2022). The ontological description of the main concepts introduced in the SustainGraph can be considered as accompanying information of the structure introduced in the LPG model. By having access to the ontological description, better semantic alignment of terms with emerging ontological specifications can be achieved, while integration of the semantics in the LPG model can be supported in the future  (Purohit et al.
, 2021). Following, we focus on the description of the labeled property graph (LPG) model of the SustainGraph. A high-level view of the SustainGraph structure is provided in Figure  4. The main set of entities in the SustainGraph has to do with the description of the structure of the UN Sustainable Development Goals (SDGs), building upon an existing formal knowledge organization system for this purpose  (Joshi et al. , 2021). According to this system, within the SustainGraph, a Sustainable Development Goal has a set of Targets, where each Target is associated with one or more Indicators (UN SDG, 2022). Each Indicator is measured based on Series of data (time series data). Each data Series is accompanied by SeriesMetadata where details for the metric that is measured is provided, while it includes a set of Observations.
To support geolocation characteristics, each Observation refers to a specific geographical Area (GeoArea). The aforementioned UN SDG indicators regard a subset of the indicators that can be represented in the SustainGraph. Further indicators along with the data series that are associated with them can be introduced. Specifically, the aforementioned structure for the representation of the UN SDGs has been generalized to support the measurement of similar indicators in the EU level, as well as indicators provided from third party sources. At the EU level, the EU SDG indicators are provided by Eurostat and can be associated with the UN SDG indicators (EU SDG, 2022). Multi-purpose indicators are defined, where one EU SDG indicator may contribute to more than one goal. Data coming from third party sources are also represented.
The SustainGraph emphasizes the capability to declare relationships among Indicators. For instance, this is applicable in the case of EU SDG indicators, where an EU SDG indicator may be similar to, part of or identical to an UN SDG indicator. Similar relationships can be declared among UN or EU SDG indicators and indicators coming from third-party sources. In this way, the relationships among indicators tracked by different monitoring frameworks are represented, enabling data interlinking and interoperability. Furthermore, a relationship is added where each EU SDG Indicator can contribute to a specific Policy Target defined at the EU level. A view of this part of the specification within SustainGraph is depicted in Figure  5. By having conceptualized the way that time series data from various indicators can be represented in the SustainGraph, we moved on with the representation of concepts coming from policy frameworks and directives to adapt policy frameworks.
A view of this part of the specification within the SustainGraph is depicted in Figure  6. Another important set of entities represented in the SustainGraph is related to the implementation of Case Studies across Europe to develop climate-resilient regions through the adoption of systemic solutions and innovations. These entities regard the Case Studies, the Climate-related Hazards that aim to tackle and the Innovations that can be adopted and applied within each case study. For each Case Study we consider information related to a short description of the main challenges, actions and envisaged impact, the set of Stakeholders involved in the Case Study and the application geographical area (GeoArea). Each Case Study is associated with the SDG Goals, Targets and Indicators, while it also includes information for Indicators defined by thirdparty data sources.
Such information may be provided by monitoring infrastructure provided within the case study (e. g. , Internet of Things (IoT) nodes, satellite images, data coming from citizen science platforms) or made available from other initiatives or monitoring frameworks (e. g. , happiness index, corruption perception index). For the Climate-related Hazards, we have adopted the classification of hazards provided by the European Environmental Agency for tracking the Europe's changing climate hazards  (Crespi et al. , 2020). 32 climate hazard Indicators are made available, organized according to 16 hazard categories, grouped into six main types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean)  (Crespi et al. , 2020). The Innovation entity is introduced to represent innovative solutions that are developed to support adaptation and mitigation measures for climate change, based on the description of such innovations in the Climate Innovation Window developed by the BRIGAID project (van Loon-Steensma, 2018).
SustainGraph data population Based on the conceptualization of the SustainGraph, a set of data population mechanisms are implemented to support knowledge acquisition processes. Through the data population mechanisms, existing data provided by various sources and different formats can be transformed to knowledge within the SustainGraph. The data population process is a dynamic process, where fresh data is continuously fed into the KG, enriching the available information and enabling further knowledge production and management, as detailed in SustainGraph data population. The main challenge faced here has to do with the development of custom scripts for automating or semi-automating the data ingestion to the KG. By making available such scripts, new releases of the considered datasets can be easily incorporated in the KG, reducing significantly the overhead posed to data scientists for continuously processing the available data to bring them into a homogeneous and interoperable format.
A wide set of data sources is considered. This set includes open data provided by international organizations, statistics authorities and public bodies in the form of tabular datasets (e. g. , files in csv format) or through open Application Programming Interfaces (APIs), data coming from the monitoring infrastructure that is implemented within case studies in various regions, and data coming from the processing of policy documents and reports. For the latter, machine learning (ML) techniques are applied to support the knowledge acquisition process. The main considered ML technique is related to Natural Language Processing (NLP) mechanisms that helps understanding the content of the documents and extract information and insights from them. NLP can be used for detecting key words and phrases relevant to Frontiers in Environmental Science frontiersin.
org the SDGs, quantify overlaps and relationships between texts  (Smith et al. , 2021). Various NLP methods can be used, including latent semantic analysis for analyzing relationships between a set of documents and the terms they contain  (Smith et al. , 2021)  and multi-label classification of texts using Bidirectional Encoder Representations from Transformers (BERT)  (Matsui et al. , 2022). Data quality assurance for the data that is introduced in the SustainGraph is challenging. Each dataset that is coming from an external source is prepared based on a set of assumptions and different processes in terms of data quality management. Even data from the same data provider may be made available following a different structure in different time periods, while the existence of outliers and null values is often noticed.
Bias may be also introduced in the provided data, based on the applied data collection and management processes and the representative inclusion (or not) of the associated groups. To improve data quality, a set of data quality assurance mechanisms are under development. Data cleaning mechanisms are applied for improving data quality, while considering bias detection in terms of fairness. Data cleaning may regard -among othersremoval of outliers, removal or completion of entries with missing values, and deletion of content that is not considered for inclusion in the KG. Bias detection in the data may regard the non-proper representation of sub-groups within the datasets or the presence of systematic differences between groups, while fairness can be assessed based on the alignment of the data with the FAIR (data that can be easily findable, accessible, interoperable and reusable) principles  (Wilkinson et al.
, 2016). It should be noted that in the SustainGraph version that is presented in this manuscript, data is introduced by wellknown and reliable data providers and, thus, simple data cleaning mechanisms are applied (outliers' removal, missing values management). Data provenance is supported, since the origin of the data is always made available in the properties of the associated Series entity instance in the SustainGraph. A list of the main data sources used for populating with data the SustainGraph is provided in Table  1. As already mentioned, this list should be considered as indicative, since the list of data sources is continuously evolving given the availability of further data and the emergence of new concepts within the SustainGraph.
SustainGraph knowledge production, exploration and evolution By having access to a data-populated version of the SustainGraph, a set of services can be offered upon it. These services include data exploration and visualization, data analysis, participatory modeling and analysis, knowledge production and KG completion. Following, we provide a short description of these services, while usage examples are provided in SustainGraph knowledge production, exploration and evolution. As already stated, the SustainGraph can be considered as a knowledge repository related to the evolution of the SDG indicators at national and regional levels. Data exploration can take place through the submission of queries by end users. Each query is related with an open question, while the query result may provide an answer.
Data exploration can be provided also through web-based navigation in the entities and relationships of the SustainGraph. Various visualizations can be produced for depicting trends in the available data, comparing metrics based on their temporal and/or spatial resolution, and highlighting the weight of the existing relationships in the graph. In this way, end users are able to explore the existing knowledge in the KG, achieve common understanding, get answers to specific questions and easily grasp trends and insights through visualizations. Moving one step further, through the submission of queries to the SustainGraph, the retrieved data can be fed as input to analysis pipelines. Such analysis pipelines may regard algorithms applied over tabular data or graph algorithms applied over the SustainGraph or a part of the SustainGraph.
Use case #1: For a specific UN SDG indicator, compare its evolution per country in the last 20 years for countries in the Mediterranean. In this use case, we examine the evolution of the UN SDG indicator 1. 1. 1 in a number of the Mediterranean countries. The indicator depicts the proportion of the population living below the international poverty line and is used for evaluation of the progress towards achieving Target 1. 1 (by 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1. 25 a day) of the SDG #1. A query is submitted to the SustainGraph to get time series data for this indicator for a set of countries. The produced output is visualized in Figure  8. Use case #2: For a specific EU SDG indicator, compare the current status of the indicator across countries in Europe, considering also the EU policy target to be achieved by 2030.