dataesr/scientific-paragraphs-categorization huggingface dataset:
This is coupled to two primary data storage locations, a simple cloud-based file store (AWS S3) providing unstructured data capacity, and an SQL database (PostgreSQL) providing structured data capacity. Alongside these technologies the scientific programming language R (R Core  Team, 2022)  provides the programmatic tools for interacting with these storage structures (read, write, report and visualise), as well as enabling the presentation of the resource to a community of web-based users via the R Shiny package  (Chang et al. , 2022). Github provides a mechanism for community engagement in the development process (https://github. com/Missing-Salmon-Alliance/). The initial metadata entries in the SalHub database were harvested from a review of existing data resources of relevance to Atlantic salmon carried out during the NASCO-ICES WKSALMON workshop on salmon mortality at sea (ICES, 2020a).
Around 100 data sources were identified and described in SalHub to provide a new organisational tool for future cooperative workshops. The number of described resources in SalHub has since increased to over 250. Of these, around one third are open data and include a URL to the data itself. The remaining entries are a combination of resources identified via publications or targeted searches for specific data sources. Whilst this approach has proven useful in painting the knowledge landscape with a broad brush to begin the process, much of the finer detail of the data representations remain absent. This will require input from data creators to enable resources to be fully utilised in future hypothesis-testing frameworks. Users intending to submit/register data resources may do so via a form designed as an intuitive and quick method to capture the level of detail required for findability and attribution.
There is a minimum required fields restriction, set at a very low level of detail (title, natural language description and contact details) to encourage engagement and enable communication between data providers and the user community. Additional and optional fields invite further description of a resource's availability, geographic coverage and temporal range. A set of user guide videos have been created to assist users with this process and links are available via the application itself. The fields found in the data submission process are derived from an existing metadata standard, Ecological Metadata Language (EML)  (Jones et al. , 2019) , a strict and structured syntax for documenting research data, which is recognised as a leading metadata standard for ecological sciences. EML is already used by many metadata catalogues, such as the KNB, ensuring every effort put into describing a knowledge resource via SalHub is interoperable with the KNB and other online data portals.
The use of EML also opens up pathways for development of SalHub that include implementing data federation capabilities (efficient mirroring of resources across repositories) and more easily leveraging technological advances made across large federated data repository networks (e. g. , the Data Observation Network for Earth (DataONE), htt ps://www. dataone. org/). This means that tools for more advanced annotations, version tracking and provenance representation will be available to data resources within SalHub with only minor developer input. SalHub already mirrors metadata to the KNB, albeit with restricted access, as part of this potential development track. In addition to creating a simple metadata catalogue, a key outcome of SalHub is to implement a data network within which described resources may be indexed, and in turn utilised, in the context of addressing core questions facing Atlantic salmon research and management.
, 2019) , and 60 subclasses (Variable Classes) based on what we have identified as descriptors for the range of variables that may influence salmon growth and survival (Fig. 3 ). These life-stage domains and subclasses are presented to the user through a tick box driven interface following on from the initial metadata forms, offering the opportunity to index any knowledge resource within this basic organisational structure. Variable Classes provide further granularity to the structure of the database indexing network. They are authored with existing definitions in mind, such as the Essential Ocean Variables proposed by GOOS (https://www. goosocean. org/) and the Biodiversity Observation Network's Essential Biodiversity Variables (https://geobon. org/ebvs/), and are either set deliberately narrow, for well-defined observable metrics (e.
This organisational structure also parallels a key aspect of data mobilisation, that of the annotation of data resources with highly defined (dereferenceable) terms describing observations or attributes within the resource. As the landscape of controlled vocabularies and ontologies is potentially confusing to the intended audience (data creators/providers), annotation addresses one of the barriers to user engagement described previously. SalHub's domain-variable class structure offers a discipline specific, and therefore familiar, mechanism to move data resources into a framework which has an emergent property similar to that of metadata annotation. Though currently lacking the formal definitions recommended by the FAIR principles, it provides a step towards more robust data description and annotations that may be deployed in future development, i.
e. , linking in with existing variable classes such as the Essential Ocean Variables (EOV), Essential Biodiversity Variables (EBV), or controlled vocabularies like the British Oceanographic Data Centre's Vocabulary Server (http://vo cab. nerc. ac. uk/collection/). The domain-variable class structure may also be utilised not only to describe data resources, but to frame research questions. It will assist in assessing the utility of multiple spatial and temporally scaled datasets for inclusion in the testing of hypothesized relationships between the declines in salmon survival and various environmental and ecosystem factors, and in considering their use as future indicators of change. Several key salmon mortality hypotheses have already been indexed within this structure so that data resources can be inherently linked to broad questions.
Browsing resources within SalHub is achieved through a set of predefined filters on separate pages. These filters are defined using a selection of mortality hypotheses, through the domain-variable class index directly, or by the spatial properties of the resources displayed on an atlas (Fig. 4 ) that also incorporates additional layers to enhance the context available to the user (e. g. , ICES ecoregions (ICES, 2020b), NAFO Divisions (https://www. nafo. int/About-us/Maps), and/or the NASCO rivers database location data (https://nasco. int/about-nascos-riversdatabase/)). The LPG provides the technical capacity for storing this organisational structure and the metadata captured through the user interface. It is also a versatile starting point for future development opportunities such as linking in with existing knowledge graphing activities (e.
g. ,  Akenhead et al. , 2021). The LPG itself is not an appropriate place to directly store data files: data and knowledge are ultimately held in digital formats and locations that depend on the nature of the entry and how the user has chosen to store them. There are four distinct possibilities for storing the resource: 1. If data are already available online a URL may be supplied within the metadata, 2. A file may be uploaded along with the metadata directly and stored within the SalHub file store, 3. Structured resources may then be passed on to the SQL database if appropriate, and 4. Data may remain with the metadata author in their own storage solution. Data availability is arranged via correspondence between the requester, the SalHub data administrator, and the owner. Given the geographic range that salmon cover throughout their life cycle, and the breadth of jurisdictions, and potential knowledge holders, data resources are numerous and widespread.
We suggest that SalHub offers novel support for future collaborative research and can contribute to the development of more effective salmon management. Benefits include: • Centralising the search for information sources relevant to Atlantic salmon. • Ready consideration of resources within multiple existing online repositories and databases, making it easier to visualise and interrogate the knowledge landscape of the Atlantic salmon, and encourage greater utilisation of valuable and hard-earned datasets. • As data sources are gathered in a single location, it becomes more achievable to create and share associated workflows from source materials to synthesis and data products. This visibility may be of use to help improve and elaborate on formal data pipelines (e. g. , formal data calls in support of cooperative stock assessment exercises (ICES, 2021a), and in assisting with bridging this activity to wider developments in ecosystem-based management.
Discussion Collaborative efforts calling for the development of suitable data mobilisation systems to assist future salmon management across their range are gaining momentum (e. g. ,  Bull et al. , 2022; ICES, 2020a; Siegle et al. , 2021; Woodward et al. , 2021). Ongoing data initiatives (such as the development of SalHub) begin to address these requirements and need to be supported. They provide guidance, and a direction of travel that can be emulated and used to promote effective management change. However, the development of data solutions faces multiple technical and operational challenges to promote widespread uptake of this, or similar, interoperable systems into current and future salmon research, stock assessment and management frameworks. Whilst it is recognised that simply indexing every potential resource in a single location will not address all the challenges of data and model limitations when there is a requirement to forecast complex ecological systems, we believe it represents a step in the right direction for the future of Atlantic salmon management.
Even in its early developmental stages it presents to the ecological sciences a unique tool to bridge the gap between ecoinformatics experts and discipline/species experts, promoting data mobilisation in a space where limited salmon-specific data tools exist. It already supports the development of the Likely Suspects Framework  (Bull et al. , 2022)  by the Missing Salmon Alliance and offers a common platform for the re-synthesis and sharing of knowledge resources, and the promotion of cooperative analysis in support of hypothesis-testing. As a general example of the database's utility, a researcher interested in processes influencing salmon growth or survival during a specific life stage may engage with SalHub by firstly selecting the appropriate filters (i. e. , domains and/or variable classes).
These will provide a list of potentially valuable source material to assess the opportunities for hypothesis development and testing. In time, further refinement of search tools will allow users to specify spatial or temporal extents. As a direct example of SalHub's current utility, research efforts to test hypotheses to determine the proximate drivers of changes in early marine survival in southern European Atlantic salmon stocks are underway using it to organise data resources synthesised from various sources. These include oceanographic models  (Atlantic Margin Model 7. O'Dea et al. , 2017; Scottish Shelf Model. Wolf et al. , 2016) , salmon marine survival data (ICES, 2021a), river conditions prior to emigration  (Hannaford, 2004)  and the abundance, composition and distribution of biota from various trophic levels e.
g. , Continuous Plankton Recorder  (Johns, 2011) , and pelagic fish species  (ICES, 2021c). Existing online data platforms aimed at salmon research and management tend to focus on visualisations of specific analysis, e. g. , the Pacific Salmon Explorer (PSE) https://www. salmonexplorer. ca/ or the Spatial Hydro-Ecological Decision System (SHEDS) https://pitdata. ecosheds. org/fish-movements/). Others, e. g. , the SalmoGlob Toolbox (https://sirs. agrocampus-ouest. fr/discardless_app/WGNAS-ToolBox/) (Hernvann et al. , 2021) , facilitate specific data (i. e. , assessment model data input and outputs) synthesis. Collating data and communicating and disseminating trends through visualisations such as those found in the PSE and SHEDS are of the utmost importance to improve public engagement in science.
Specific and live syntheses such as SalmoGlob demonstrate novel and critically important pathways to maintaining upto-date sets of time series data and indirectly provide support for greater communication on the functioning and capabilities of assessment models, fostering the integration of new data in the latter. SalHub performs a unique function in this space, offering strategic enrichment of the knowledge base underpinning current and future salmon research, serving knowledge exchange and resourcing the research activities that these platforms (and others) may potentially draw benefits from. Development of SalHub is still in its infancy, and several limitations need to be addressed moving forward. It has been launched with the understanding that its long-term success is reliant on the acceptance and use by the wider salmon research community.
There is a requirement to consider expansion within the resource to capture valuable fisheries focused Indigenous Knowledge (e. g. ,  Reid et al. , 2021; Sethi et al. , 2011) , and it would be pertinent to explore what community driven solutions (such as SalHub or similar) could offer this effort. A critical approach to the usage, analysis and integration of the resources registered in SalHub by the scientific users will require maintenance, as would be expected regardless of data source. Building closer ties to existing data repository networks (e. g. , DataONE) will enhance longevity and ensure that resources can remain sustainable by providing persistent metadata, whilst maintaining a degree of freedom to evolve capabilities that can emerge from community interaction in the development process.
Recommendations for improving data mobilisation for salmon management The scale of progress required to address the challenges facing Atlantic salmon will require considerable interdisciplinary efforts and a major shift towards improved data integration in order to develop wider ecosystem-based approaches in how salmon stocks are being assessed and managed. It is hoped that data owners populating new resources into SalHub will be encouraged by the opportunities presented to contribute to cooperative research initiatives in the pursuit of improved future salmon management. Further to this altruistic motivation, there are tangible benefits for data owners via increased co-authorship opportunities and direct data citations  (Data Citation Synthesis Group, 2014). For the full potential of SalHub to be realised, extensive cooperative efforts from within the salmon science and management communities will be required in populating knowledge resources and expanding them to include wider sources alongside newly synthesised products.
Regular updates and reliable submissions of data from key sources will assist with the promotion, uptake and use of the resource in the science communities, whereas recognition and endorsement by statutory bodies such as NASCO, and established expert groups (e. g. , ICES Working Group on North Atlantic Salmon (ICES, 2021a)) would help marshal resources towards addressing critical management needs. By cooperatively building a data mobilisation network and a usergroup that is truly multi-disciplinary, SalHub can provide a new space for innovative data pipelines to be developed and shared, ensuring widespread incorporation of high-quality synthesised products. Assisted and guided by these efforts the synthesis of a wide range of biotic and abiotic indicators for use in stock assessment  (Harvey et al.
With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances' energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset.
Introduction Nowadays, an increasing amount of individuals own wearable sensors (e. g. , in smart phones or smart-watches) or ambient sensors that are deployed in their homes. The raising availability of raw sensor data motivates new services to offer insights about the health of their customers or about the energy consumption of appliances in their home. Sensor data collection is under the consent of the service customers and defined in a legal framework, such as the GDPR (http://data. europa. eu/eli/reg/2016/679/oj, accessed on 9 September 2022) (General Data Protection Regulation). In other words, the sensor data are collected for some given purposes. Moreover, in recent years a diversity of data mining algorithms have been proposed in order to extract, from collected sensor data, information related to individuals.
Although those algorithms can be used for genuine purposes, they can also be leveraged to gain insights breaching individuals' privacy  [1]. For instance, Banos et al. [2]  and Chikhaoui et al. [3]  demonstrated on the MHEALTH and the Opportunity datasets, respectively, that one can infer human activity from specific wearable sensor observations, if the observation interval exceeds a minimum temporal duration. Similarly, Cumin et al. [4]  and Shahi et al. [5]  demonstrated on the Orange4Home and the CASA-Aruba datasets, respectively, how daily human activities can be inferred from ambient sensors, thus considering spatial constraints. Aside from human activities, sensor data can be exploited to infer the behavior of individuals  [6] , the PIN (Personal Identification Number) code of a smart-phone  [7] , or to authenticate individuals based on keystrokes dynamics  [8] , etc.
In any case, the human activities can be genuinely inferred for a purpose that the customers are consenting to (e. g. , obtaining insights about the energy consumption of appliances, based on the human activities  [9] ), while being diverted to a non-consented use. The diverted usage of mining algorithms to obtain more data on individuals is kindred to inference channels in the domain of databases, where it is defined as "the ability to determine sensitive data using non-sensitive data"  [10] , directly or indirectly as illustrated by the motivating example in  [11]. Therefore, this usage can be considered as a kind of inference channel that we will call inference attacks involving raw sensor data (IASD) in the reminder of this paper. In the literature, the inference issue in relational databases is addressed via inference detection systems (InfDSs)  [10, [12] [13] [14] [15] [16].
To be able to detect inferences, the detection process needs to model the set of inference channels beforehand. Then, it gathers a knowledge about the data already queried by a user, and decides whether to answer the query or deny it in case of inference. In InfDS dedicated to detecting inference attacks in relational databases, inference channels can be universally represented by the same formalism (e. g. , capturing functional dependencies (FDs) using Horn clauses  [10] ). However, inference channels involving raw sensor data have some additional specificities, such as temporal constraints which make existing modeling techniques not suitable. The first requirement that an InfDS must meet for those kinds of inference channels is related to the representation of constraints. A mining algorithm is able to extract information from sensor data if constraints, with respect to those data, are satisfied.
Consequently, the target model must be easily extensible in order to be able to capture this diversity of conditions related to the new discovered inference channels, as well as enabling an efficient reasoning on inference channels to detect when a user's knowledge enable her to perform an inference attack. The InfDSs proposed in the literature model inference channels without considering constraints with respect to the data. Existing InfDS solutions are thus not suitable to model inference channels involving raw sensor data. Hence, the first contribution of this paper is the Raw sensor data-based Inference ChannEl Model (RICE-M), a new model which enables representing the conditions on sensor data, as well as the user knowledge gained by users querying sensor databases. Based on this model, we propose an InfDS, called RICE-Sy, which implements the detection of inference attacks at query-time (i.
e. , before the query is answered by the database  [17] ). We formally describe RICE-Sy's principle and propose a proof of concept which demonstrates the feasibility of detecting inference channels involving raw sensor data. For this purpose, we use as a case study MHEALTH, the inference channel described by Banos et al. [2]. The second requirement is related to the capability of the InfDS to perform the detection at query-time while processing efficiently huge quantities of user knowledge. Sensors produce a continuous stream of data that are then stored in databases and performing the detection increases the total query answer time. Hence, the proposed InfDS should have an overhead as small as possible. The second contribution of this paper is a set of conceptual optimizations for RICE-Sy, which aim at reducing the quantity of user knowledge that must be considered when performing the detection on a query.
Related Works In the following, we first present the few related works associated with the detection of inference attacks stemming from sensor data, as well as highlighting the limits of the solutions, with respect to the requirements presented in Section 1. Noury et al. [18]  propose an access and inference control model which enforces value constraints and temporal constraints on time series databases. The access control model they propose enables the definition of the rules which state the kind of permitted computation on time series and for which time interval. In their context, an inference attack occurs when at least two conflicting access rules lead to some temporal intersection which enable dishonest users to infer values that must be protected by the access control mechanism. Hence,  Noury et al.
do not propose a model to represent inference channels within the database. Furthermore, the proposed inference detection occurs at the design stage, i. e. , before receiving queries on time series and it cannot operate at query time as required. Due to the few works related to the detection of inference attacks involving sensor data, in the remainder of this section we present the InfDSs targeting personal databases protection only. Farkas et al. [17]  show that two approaches are followed by InfDSs to prevent attacks. The first one is at design-time, where the schema of a relational database is modified to prevent the exploitation of inference channels. Turkanovic  [19]  shows that completely removing the inference channels is an NP-Complete problem. Furthermore, the schema of databases is not always available and even less modifiable.