dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis We calculated the ICH score, 10 modified-ICH score,  11  max-ICH score  12  and the FUNC score  13  (table 1), at initial presentation and in a delayed fashion. We calculated 'baseline' ICH scores using clinical assessments on initial presentation and CT scans used to confirm ICH. We calculated 'delayed' scores using clinical information and CT scans obtained after the last infusion of DFO or placebo. In the iDEF trial, infusions were started within 24 hours of presentation, and continued over a 3-day period, which means the delayed scans in our study were calculated at 72-96 hours after initial presentation, well after the known timeline for haematoma expansion. 15 Our outcomes are mortality assessed at 30 days, 90 days and 180 days, and poor functional outcome in survivors at the same time points, defined as mRS of 4-5.
] [31]  Our study has important limitations. While our study supports waiting until at least day 4 to prognosticate, it does not mean a decision needs to be made at that time. Waiting even longer to make decisions regarding withdrawal of care may be appropriate in specific clinical scenarios-for example, in the case of significant mass effect during early days. Next, the iDEF trial only included patients with supratentorial haemorrhages who were not on anticoagulation and had milder clinical presentations (ie, GCS >6), thus skewing our scores towards a patient population with mild-moderate ICH (see online supplemental figure I). More specifically, the results of our study cannot be extrapolated to infratentorial haemorrhages, as we did not have access to data from this subpopulation in the current analysis.
Therefore, our results are difficult to generalise to a general patient population with ICH, as a clinical trial population naturally selects for patients who are less clinically severe compared with an unselected pragmatic population. Conversely, our patient population provided well-characterised clinical and radiological Open access data with multiple time points for outcome assessments which allowed for a thorough and assessment of multiple prognostication scores. While an interaction was observed between treatment effect and FUNC score, we suspect that this interaction is statistical in nature, given the lack of interaction with the remaining scores and the relative similarities in the individual components that make up each score.
Furthermore, van Klompenburg, Kassahun and Catal have performed a systematic literature review (SLR) about the ML algorithms and features used in crop yield prediction studies  [7]. The results show that no specific conclusion can be drawn as to what the best model is, but they clearly show that some ML models such as the random forest, neural networks, linear regression, and gradient boosting tree are used more than the others. Additionally, Noon et al. have presented a large survey of the application of deep learning (DL) techniques for plant leaf stress identification  [8]. The techniques reviewed were divided in vegetables, fruits and other crops on the basis of stress type, size of dataset, training/test size and the deep network used. Several research gaps have been identified, such as the detection and classification of all severity levels of a stress to detect contagious diseases, or the generalization of algorithms to different crops.
The initiative aims at the creation of a dataset by the fusion of sensor data from machines, remote sensing (satellites and drones), soil, weather and other existing data sources  [12] , so that agricultural processes like irrigation, fertilization, or pest control can be optimized thanks to the application of advanced AI methodologies. An important focus of the initiative is the visual analysis of plants and fields from images captured by satellites  [13] , drones or agricultural vehicles. Furthermore, the combination of multispectral imaging with radar and soil sensors addresses the determination of plant and soil condition  [14]. Finally, powerful AI algorithms will fuse the data and extract information for the optimization of the agricultural processes. The initiative will target two open research challenges within the AI community.
On the one hand, even though methods based on AI have shown precise performance, they degrade with small training datasets (e. g. , when trying to detect rare events or diseases on plants). Thus, domain specific knowledge and semantic models should be integrated into the AI methods to increase precision. On the other hand, AI algorithms look like black box systems for farmers, who find it difficult to understand results, reducing acceptance of such solutions. Therefore, methods for explainability will be exploited to validate network decisions and to improve training and network generalization. Finally, it should be mentioned that the initiative will also focus on the potential problems arising from data privacy with cloud-based agriculture as data will be shared among different stakeholders.
Thus, the development of AI methods and services must always ensure data sovereignty along the entire value chain and interoperability between An important focus of the initiative is the visual analysis of plants and fields from images captured by satellites  [13] , drones or agricultural vehicles. Furthermore, the combination of multispectral imaging with radar and soil sensors addresses the determination of plant and soil condition  [14]. Finally, powerful AI algorithms will fuse the data and extract information for the optimization of the agricultural processes. The initiative will target two open research challenges within the AI community. On the one hand, even though methods based on AI have shown precise performance, they degrade with small training datasets (e. g. , when trying to detect rare events or diseases on plants).
Agricultural Digital Twins for Geospatial Monitoring Within the manufacturing domain, the digital twin (DT) concept allows for testing hypotheses through simulations, establishing continuous monitoring and implementing measures for management and maintenance. Although agriculture cannot be compared one-to-one with a factory production line, this concept can be extended to fulfil similar expectations as in the Industry 4. 0. An agricultural DT [15] represents a generic key component for agricultural applications to build up a reliable database for plantations and fields, to increase accuracy and coverage of crop prediction, and to significantly reduce costs and time expenditure. Acting as a virtual replica of the physical system, the DT provides the ability to examine complex scenarios which involve multiple, intertwined factors at once.
g. , by surveying offices). However, a single point of the 3D point cloud does not allow constructing meaningful feature vectors. Therefore, geospatial AI algorithms based on ML and DL have been implemented in the German project DEAL to automatically derive vegetation objects from 3D point clouds so that a fully automatic workflow that generates an agricultural DT can be implemented (Figure  2 )  [17]. In such context, geospatial AI offers manifold ways to "distil" object-based information from 3D point clouds, as ML and DL algorithms are able to identify and interpret orchards and tree plantations, including the whole range of what constitutes "trees" and "vegetation" (e. g. , "crooked trees", "flat bushes", "diseased trees", etc. ). An agricultural DT [15] represents a generic key component for agricultural applications to build up a reliable database for plantations and fields, to increase accuracy and coverage of crop prediction, and to significantly reduce costs and time expenditure.
AI-Based Irrigation to Optimize Water Use The European project Internet of Food and Farm 2020 (IoF2020) explores the potential of the Internet of Things (IoT) technologies for the European food and farming industry to make precision farming a reality  [18]. IoT-driven PA enables farmers to optimize their operating tasks to increase crop yields and to minimize costs of inputs such as water, fertilizer, insecticides, and herbicides. The project has implemented the QUHOMA platform  [19] , a smart irrigation solution developed as a FIWARE-backed end application (Figure  3 ). The irrigation schedule is calculated by a complex event processing (CEP) AI approach based on current algorithms used for irrigation. This approach relies on the daily accumulated sensor data. Therefore, if the data stream were interrupted or inaccurate due to malfunction or failure of the sensors, and/or loss of connection during long-time periods, calculations would deviate from the optimized target.
tilizer, insecticides, and herbicides. The project has implemented the QUHOMA platform  [19] , a smart irrigation solution developed as a FIWARE-backed end application (Figure  3 ). The irrigation schedule is calculated by a complex event processing (CEP) AI approach based on current algorithms used for irrigation. This approach relies on the daily accumulated sensor data. Therefore, if the data stream were interrupted or inaccurate due to malfunction or failure of the sensors, and/or loss of connection during long-time periods, calculations would deviate from the optimized target. To overcome these limitations, the project has implemented AI-based predictive analytics that fuse the gathered datasets with other public data sources to train models. As a result, data customized to the field are generated and used as a workaround for the following two scenarios.
100 days), planted in a tunnel farm with Clay Loam soil in the coastal Ammochostos district in Cyprus (Figure  4 ). In order to perform this experiment, critical values of the climate zone, ETo (reference evapotranspiration), ETc (crop evapotranspiration) and other sensitive irrigation parameters were implemented on the algorithm which handles the smart irrigation schedule on QUHOMA. Several tasks such as quality assessment and cleaning of weather data, or the To overcome these limitations, the project has implemented AI-based predictive analytics that fuse the gathered datasets with other public data sources to train models. As a result, data customized to the field are generated and used as a workaround for the following two scenarios. On the one hand, sensor data streams may become unavailable for a period longer than the estimated tolerance threshold of the CEP algorithm.
The EU-funded project DEMETER is developing prediction models of the phenological state based on the day of the year (DOY) and GDD using ML algorithms. Such models have been trained and tested with data collected during the monitoring of phenology in olive orchards in Tuscany (Italy) in 2008-2010. For each olive orchard, five olive trees in good health conditions and with homogeneous productivity were selected. The monitoring consisted in visiting the orchards at different time spans during the year, according to the development stage of olive trees in the different seasons  [24]. There are 821 data points in the dataset that belong to 22 different locations with an average of 34 data points per location and 274 data points per year. The dataset was split.
The EU-funded project DEMETER is developing prediction models of the phenological state based on the day of the year (DOY) and GDD using ML algorithms. Such models have been trained and tested with data collected during the monitoring of phenology in olive orchards in Tuscany (Italy) in 2008-2010. For each olive orchard, five olive trees in good health conditions and with homogeneous productivity were selected. The monitoring consisted in visiting the orchards at different time spans during the year, according to the development stage of olive trees in the different seasons  [24]. There are 821 data points in the dataset that belong to 22 different locations with an average of 34 data points per location and 274 data points per year. The dataset was split into training (60%), validation (20%), and test (20%) disjoint sets with the particularity that data were chosen using stratified sampling from the data for the different years.
It overcomes some of the constraints of current autonomous vehicles or robots working on agricultural environments, such as overly repetitive patterns, the need of very detailed reconstructions, or the abrupt movements caused by uneven roads. The results of the project have been extended towards the agricultural sector. Therefore, experiments have been conducted over the Rosario dataset, which consists of six sequences recorded in a soybean field, captured by forward looking stereo camera, showing real and challenging cases such as highly repetitive scenes, reflection and burned images caused by direct sunlight and rough terrain, among others. In our approach, the dense point cloud can be generated offline up to real scale (see Figure  9a ), so that it is suitable for tasks like 4D monitoring of the crop and the provision of valuable information to the farmer (e.
First, as low cost and accurate monitoring of crop and soil health has long been key to a successful agricultural economy, two examples of current German projects that deal with the availability of agricultural data and its potential use in DT to provide insights into such complex systems have been described. On the one hand, the large scale NaLamKI initiative will lead the implementation of a dataset by the fusion of sensor data from machines, remote sensing (satellites and drones), soil, weather, and other existing data sources to overcome the current limitation of data availability when implementing advanced AI methodologies for the optimization of agricultural processes. Furthermore, current research challenges for AI such as explainability and the integration of semantic agricultural models will be targeted.
On the one hand, AI applications in agriculture such as for irrigation, weeding or spraying, save the excess use of water, pesticides and herbicides while maintaining the fertility of the soil to improve both productivity and quality of produced goods. In this way, the QUHOMA irrigation platform developed within the IoF2020 project has been extended with AI-based predictive analytics that fuse the gathered datasets with other public data sources to train models when data stream is interrupted or inaccurate due to malfunction or failure of the sensors, and/or loss of connection during long time periods. Real-life experiments conducted in strawberry crops in Cyprus led to a significant reduction (from 6 to 11% less) in water used when compared to the empirical irrigation scheduling program available for the farmer.
The platform is currently being adapted to groves of olives in order to optimize their irrigation using the minimum number of sensors. On the other hand, while many analytical models have been developed to plan agronomic strategies to optimize production, AI leads current research since it enables to take into account complex multidimensional holistic approaches. This paper describes two examples of the use of ML techniques for the yield prediction, both in the automated estimation of canopy features of an experimental vineyard in Lisbon and in olive tree plantations based on phenotypic data. First, the VINBOT project aims at predicting yield in vineyards using an all-terrain AGV with a set of sensors capable of capturing and analyzing vineyard images and 3D data by means of cloud computing applications.
Therefore, groups of heterogeneous intelligent and autonomous agricultural vehicles (UAVs) and ground robots (UGVs) capable of approaching and recognizing, manipulating, sampling and collecting, transporting and delivering soil and plant samples required for inspection should be developed. High-quality sampling data gathered by robots will allow the development of novel DDS, which in turn will facilitate decisions on exactly how, where and in which amount inputs should be used to improve production and to use resources sustainably. The concept, once applied, will greatly increase precision and quality in farming processes. Furthermore, future extensions of this work will include the implementation of AI algorithms for the smart farming concept, including effective livestock management. For example, automated video monitoring of animal behavior to detect events like calving, heat periods or other physiological phenomena, is of great interest to help farmers and breeders improve efficiency in their professional activity.
Indicator aggregation can also be adapted to better and more precisely inform policy and management at different resolutions. Hence, the EBV concept provides a framework for developing indicator and monitoring systems, whereas the ECT provides a conceptual framework for using and combining indicators to assess ecological condition. However, the EBV does not propose approaches for aggregating indicators (e. g. to assess the overall ecological condition) and the ECT has merely started testing out its practical implementation using empirical datasets (https://seea. un. org/). In this paper, we present a newly developed index-based ecological condition assessment (IBECA) that combines a theoretically-derived general framework for defining indicators of good ecological state with a quantitative approach to derive and aggregate relevant empirical data from monitoring  (Nybø and Evju, 2017).
Indicator aggregation and uncertainty Indicator values are aggregated at two levels, i) within each of the seven characteristics of ecosystem condition, and ii) all together for an overall ecosystem condition estimate. IBECA uses a flat aggregation approach (cf. hierarchical aggregation, described in 2. 2), where the overall assessment is conducted separately from the allocation of indicators into characteristics (Fig. 3 ). For each aggregation, individual indicator values larger than the reference value (scaled value > 1) are truncated to value 1, and the mean aggregated condition value is extracted. IBECA uses a bootstrapping approach to approximate uncertainty associated with the data for each indicator, allowing for inclusion of different data types (cf. case study dataset in Table  A1 ).
For quantitative datasets, each raw indicator dataset is bootstrapped (n = 10 000), resulting in indicator distributions yielding the median as the indicator estimate and 0. 025 and 0. 975 quantiles as the 95% confidence interval. If there is no original uncertainty associated with the data (i. e. not estimated from sample-based indicator data), IBECA implements either i) an elicitation process based on expert knowledge and published literature, or ii) a qualitative approximation of proportional (%) uncertainty (Figure  A1 ). No uncertainty is allocated to the reference and limit for good ecological condition values as these are regarded as normative values based on best available knowledge (cf. Certain et al. , 2011 , Pedersen et al. , 2016). To account for uncertainty in aggregated estimates for the seven characteristics and overall ecosystem condition, data values are re-sampled from each indicator distribution.
Analysis First, we performed a case study in which we assessed the ecological condition of forest and alpine ecosystems in Trøndelag county, central Norway, using the IBECA approach  (Nybø et al. , 2019). This assessment was based on empirical data on eleven indicators for each ecosystem, compiled from the national forest inventory  (Tomter et al. , 2010 ), a regional vegetation monitoring project, species-specific monitoring programs, and mapped spatial data to calculate regional indicator values (Table  A1 ). Hence, data were not collected at a common scale with a common methodology, but assembled from different sources as relevant indicators of different characteristics of ecological condition for each of the two ecosystems. Representativity varies, as some data are from established long-term monitoring programs (e.
, 2018)  in monitoring programs has been suggested, and in particular, the rise of open trait databases now offer promising opportunities  (Gallagher et al. , 2020). The EBV class ecosystem structure largely relies on indicators of the extent of various types of vegetation  (Pereira et al. , 2013) , but we exclusively used structure indicators linked to what we regard being indicators of the condition per se (i. e. not the extent of general vegetation types). Although  Vihervaara et al. (2017)  included nutrient content indicators within the EBV class ecosystem structure, we argue that our vegetation indicators for light and nitrogen should instead be incorporated as proxies for ecosystem function (primary production). In general, our definition of abiotic factors does not suit the EBV classification system due to the biodiversity focus within the EBV, and hence our indicator of nitrogen deposition did not match any EBV class.
Hence, the failure to capture indicators within the ECT class functional state was not surprising as it has already been highlighted that many potential EBV indicators fall into other ECT classes by definition  (Czúcz et al. , 2019). Concerning the structural state class, we acknowledge the lack of data based on remote sensing in the IBECA case study and believe that future development of the indicator set will help fill this gap (see 4. 4). The remaining three classes of the ECT were well represented by the IBECA case study indicator set, which is not surprising with the high degree of characteristic/class overlap (i. e. we selected case study indicators to represent the IBECA characteristics, such that indicators for characteristics that resemble the ECT classes naturally cover also these classes).
Currently, the ECT class compositional state represents the bulk part of the indicator set, among which many are directly or indirectly regulated by harvesting. We emphasize that using indicators sensitive to different pressures provides an opportunity to address the pressure-state relationship in the ecosystems, for example with subanalyses on which pressures most strongly influence the ecosystem's condition (Figs. 5  and 7 ). The EBV classification scheme to a large extent corresponds to the IBECA approach, emphasizing main ecological processes. However, as the two schemes emphasize different aspects of ecological processes within ecosystems, applying the EBV classification to the IBECA case study data results in scarcely covered EBV classes. In particular, indicators of genetics and traits were lacking within the case study data, while some data ended up unused in the reclassification.
Recalculations/communication Recalculating the IBECA results based on alternative classification frameworks highlighted some key differences between these frameworks. For example, the landscape category used both for the IBECA characteristics and the ECT classes is absent from the EBV classification, blurring the difference between the two ecosystems concerning landscape ecological patterns. Nevertheless, given the dataset used, all the frameworks generate similar assessments of the condition for the following general ecosystem aspects: species indicators/biodiversity ('marginally reduced'), landscape/ecosystem structure ('no significant reduction' for the alpine ecosystem, 'significantly reduced' for the forest ecosystem), and abiotic conditions/functions ('no significant reduction'). The clearest difference that emerges from the reclassification exercise conducted in this study relates to the representation of structural attributes of an ecosystem.
Future directions Continued and improved monitoring of ecological indicators is essential to understand changes in ecosystem condition, and thus to address targeted and cost-effective measures to mitigate negative trends for biodiversity  (Tittensor et al. , 2014). Based on our results we emphasize key steps forward concerning regional and national level indicator data for assessing ecological condition. First, the majority of the indicators within the IBECA case study were species indicators based on measures of abundance of species and structures well represented by ongoing monitoring programs. For future ecological condition updates, a key step for better representation within and across trophic levels is to develop additional indicators based on species community data  (Nybø et al. , 2019).