dataesr/scientific-paragraphs-categorization huggingface dataset:
The IoTool technology stack consists of Java/SQLite on the smartphone, Kubernetes/Docker with a PostgreSQL database, and a Mosquitto MQTT broker and client, node, node-red, and PHP. There were two categories of sensor devices used in the monitoring scenario in prevalidation: well-being and environmental sensors. Although IoTool supports the integration of more than one hundred different devices, a particular device choice was made not only based on technical integration capabilities but also on other aspects, such as budget constraints and market availability (considering that commercial third-party devices were used). IoTool is an IoT solution incorporating a client, a dashboard, a gateway, and a Cloud. A smartphone or other device, such as a microcontroller, is a data collector and optionally a dashboard, controller, and a gateway to synchronize data to the Cloud.
The collected data is encrypted, stored, displayed, processed, and synchronized to the Cloud (IoTool servers, self-deployed servers, or other IoT platforms, such as IBM Watson). The IoTool technology stack consists of Java/SQLite on the smartphone, Kubernetes/Docker with a PostgreSQL database, and a Mosquitto MQTT broker and client, node, node-red, and PHP. The SmartHabits Platform is based on microservice architecture, providing great flexibility for responding to the needs of different scenarios. It is a Java enterprise solution based on the Spring Framework and other open-source technologies. Time-series sensor events, as recorded observations correlated to the time received from IoTool, and context events are stored in the MongoDB databases, while other contextual and configuration information are stored in the MySQL databases.
It is a Java enterprise solution based on the Spring Framework and other open-source technologies. Time-series sensor events, as recorded observations correlated to the time received from IoTool, and context events are stored in the MongoDB databases, while other contextual and configuration information are stored in the MySQL databases. The services expose their functionalities primarily via REST (REpresentational State Transfer) APIs use lightweight JSON (JavaScript Object Notation) as a data interchange format. All APIs are secured by JWT (JSON Web Tokens) and TLS (Transport Layer Security). Apart from using (operating-system-and programming-language-agnostic) APIs, the AMQP (Advanced Message Queuing Protocol) is also used for the intra-platform components' communications. More technical details regarding the SmartHabits Platform in the scope of the Pharaon project have already been described in our previous work  [34].
Discovery is based on a microservice architecture, making it more flexible regarding development languages and for the separation of concerns. Additionally, it is possible to scale it horizontally and downtime is less of an issue, given that at least one backup service is running for every microservice. The microservices are realized with different technologies, such as PHP, ASP. NET Core, Angular, and NodeJS/TypeScript. Information is pulled via a scheduled leecher service, which transforms the information into a format compatible with the analyzer component. These leechers are small services which can be created for every new source and can also be started externally if needed. The analyzer service creates knowledge out of this information and provides it to the API. This API is integrated into a DSS (Decision support System) called Discover UI, which has several methods (diagram types, etc.
Activities, experiences, and lessons learned in the integration and deployment of technology in Pharaon have been described in  [34]. As was mentioned in the article, achieving technical (syntactic) interoperability between these technologies, which includes the agreements between the data formats, protocols, adaptation of interfaces, and APIs, was just one segment of the overall technical and supporting activities necessary to achieve end-to-end integration. Despite the simple data flow presented, each technology block is a complex system, usually running microservices in a multi-cloud environment and using a vast number of additional components such as proxy servers, firewalls, load balancers, message brokers, cache servers, Structured Query Language (SQL) and NoSQL (not only SQL) databases, and event streaming platforms.
In the first part of the meeting, every single participant was asked to provide evidence of the things that worked well (or did not work well) regarding services, technologies, and methodologies (i. e. , training, installation, and timing activities). There was then a preliminary discussion, and the notes/actions were grouped, discussed, and prioritized according to their feasibility and importance. The MURAL software ® was used to facilitate data collection and remote active collaboration between partners. Two facilitators were present in the role of facilitating interactive parts and taking notes.
To show any differences among the three categories of users, the Kruskal-Wallis test was used for independent samples due to the small sample size. To investigate the RQs, the Pearson correlation coefficient (R p ) was calculated only if both variables were individually normally distributed; otherwise, Spearman's rank correlation coefficient (R s ) was computed. In all cases, a significant p-value was held when the type 1 error rate was smaller than 0. 05. The statistical analysis was performed with SAS ® OnDemand for Academics. The small sample size has not allowed us to carry out further statistical analysis. Considering the qualitative feedback, the diaries collected from caregivers and the oral feedback collected during the questionnaire administration were read, transcribed, and analyzed to corroborate the quantitative analysis.
a b s t r a c t This data paper presents the values of CO 2 solubility at different temperatures and main compositional parameters (protein, fat, moisture, sugars and salt content) for food products from different categories: dairy products, fishes and meats. It is the result of an extensive meta-analysis gathering the results of different major papers published on the domain on the period of 1980 to 2021, presenting the composition of 81 different food products corresponding to 362 solubility measures. For each food product, the compositional parameters were either extracted directly from the original source, or extracted from open-source databases. This dataset has also been enriched with measurements made on pure water and oil for comparison purposes. In order to ease the comparison between different sources, data have been semantized and structured by an ontology enriched with domain vocabulary.
Value of the Data • Collection of a significant and up-to-date complete dataset including the CO2 solubility, as well as the measurement temperature and the food product's composition. When missing, the composition was retrieved from the Multi-DB tool, which helped us to find composition of similar food products by compiling several open-accessed databases. • In order to ease the comparison between the different format, all data have been semantized and structured by a single ontology, which enrich it by adding new queryable metadata. • All data from this dataset can be traced to their original source, guaranteeing a trackability of the different information, in accordance of the FAIR data principle. • Due to its bacteriostatic effect, CO2 is a key component for modified atmosphere packaging.
However, the experimental determination of its solubility is long and costly. This dataset offers an exhaustive collection of state-of-the-art results which can greatly benefit ongoing research. • All the stakeholders of the food engineering field can benefit from this dataset for modelling gas transfer in food. These data could also serve as benchmark for other researchers dealing with characterization and modelling of CO2 transfer in food. • In addition of the food engineering aspect, this dataset is also of interest for other knowledge bases that can be enriched with Linked Open Data.
Objective This dataset aims to improve a previously published dataset  [2]  composed of 72 measures of CO 2 solubility by adding new information (the food products composition) as well as 290 new measures taken from the literature. This original dataset presented CO 2 measures made only on cheese products. In this new dataset, we define a complete and more exhaustive base (by extending the range of presented food products) from which models could be designed in order to link a food product's composition to its CO 2 solubility  [1]. Moreover, the work done on the semantization of its data (using the @Web ontology) defined a knowledge base that can be further augmented, either by adding new measures or linking it to other knowledge bases. Each data can also be easily associated to its source, which allows an easy comparison of the results.
Thus, the main objective of this dataset was to provide a way of browsing complex and heterogenous data from multiple sources on the subject of food products' CO 2 solubility. Due to the complex nature of its evaluation, CO 2 solubility's measure is costly and time consuming. This article aims to help ongoing research by providing an easy-to-access and to-browse dataset in order to help developing new evaluation method to predict this value for different kinds of food products. This represents a major breakthrough for modified atmosphere packaging studies, and thus food products' conservation and preservation. From a long-term perspective, this dataset can help evaluate new kinds of packaging as replacement of the plastic current ones, and thus help design new, more environmentally friendly packaging.
Data Description This dataset is the updated version of a previous dataset  [2]. It introduces 362 CO 2 solubilities measured over 81 different food products (distributed over the cheese, meat, and fish categories, plus water and oil). For each solubility, its corresponding measurement temperature was also kept, since according to the corresponding literature it has a strong impact on the measured value  [3]. Each food product is described by its nutritional composition: the five most important constituents (water, fat, proteins, salt and sugars) are kept. Fig. 1  presents an overview of the diversity of our products' compositions. It is important to note that not all combinations of these compositional parameters are represented due to the physical-chemical limit of food products: for instance, products with a salt quantity over 5% are rather scarce.
The knowledge graph can be accessed manually through the @Web interface ( https://data. inrae. fr/dataverse/atweb ); it can also be directly queried using the SPARQL language in the accessible SPARQL endpoint ( https://ico. iate. inra. fr/fuseki/annotation/query ). However, for the sake of simplicity, we also provide in this article a csv file referencing our main contributions: for each solubility, we indicate its unit in the original article, the temperature of measurement, the food product considered and its composition (as well as the source if the composition is not from the original paper). We also indicate the article's title, authors and date of publication, and finally its @Web identifier. This csv file is supported by the Python script used to build it, which details relevant SPARQL queries.
Experimental Design, Materials and Methods We first considered measures of CO 2 solubility from the original article. To extend this first dataset, a corpus on the subject of food products CO 2 solubility has been defined, using the PRISMA guidelines ( http://www. prisma-statement. org/ ). This corpus is the result of an extensive monitoring by three reviewers on the Web of Science ( https: //www. webofscience. com/ ) using the keywords "CO 2 solubility" and "Food", conducted twice a year over 2016 to 2021, to constitute a database of 21 references from 1980 to 2021. From this corpus, CO 2 solubilities were extracted as well as their temperature of measurement (all units were kept from the original source), and added to the original dataset. In a second time, we manually complemented each food product with their composition in principal food nutrients (fat, water, proteins, salt, sugar): -If directly available in the article, it was retrieved as such.
2023The Authors. Published by Elsevier Inc. Specifications Table Subject Biochemistry Specific subject area Food science and food engineering Type of data Table How the data were acquired Solubility data and their temperature of measurement were acquired through an extensive monitoring on the Web of Science ( https://www. webofscience. com/ ) by three reviewers using the keywords "CO 2 solubility" and "Food" conducted twice a year over 2016 to 2021, which constituted a database of 21 references from 1980 to 2021. We focused on three types of products: cheese, meat and fish. For all measures, compositional data was added from the articles if available. In case of missing (or, in some cases, incomplete) information, the dataset was complemented using the MultiDB explorer tool ( https://ico. iate.
inra. fr/meatylab ), that compiles several diverse open-sourced databases. Using the food product's appellation, we manually selected, in the MultiDB explorer tool [27] , the food product that seemed the closer to the one mentioned in the original article in order to retrieve the corresponding compositional parameters (water, fat, proteins, sugar, salt) to fill in the database. The dataset was also enriched with CO 2 solubility measurements and their temperature on pure water and oil. Data format Analysed Description of data collection Data are structured by the @Web Ontology, which allows to describe relations between concepts. We have 362 CO This is an open access article under the CC BY license ( http://creativecommons. org/licenses/by/4. 0/ ) 2 solubilities, temperatures and nutritional composition for 81 food products from 21 publications.
All values (solubilities and compositional values) are linked to their sources: in case of missing value in the publication, the name of the open-sourced database used to complete data is indicated. Data source location INRAE, PLANET-IATE, 1208. Agropolymers and Emerging Technologies Facility, Montpellier FR-34,060, France https://doi. org/10. 15454/1. 5572338990609338E12 Data accessibility Data are accessible in a public repository Repository name: INRAE dataverse ( https://data. inrae. fr/ ) Data identification number: 10. 15454/4SFE64 Direct URL to data: https://doi. org/10. 15454/4SFE64 Related research article M. Münch, V. Guillard, S. Gaucel, S. Destercke, J. Thévenot, P. Buche, Composition-based statistical model for predicting CO 2 solubility in modified atmosphere packaging application, J.
Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality fullvolume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping.
Introduction In recent years, image data production in the biological sciences has witnessed dramatic growth. In Electron Microscopy (EM), the necessity of acquiring large volumes of tissue with the highest possible detail ignited the interest in volume-based EM methods acquired by Scanning Electron Microscopy (SEM)  (1) (2) (3). In comparison to volume datasets obtained by transmission EM tomography-based approaches, volume SEM datasets achieve lower resolution yet a substantially larger field of view. The most popular volume SEM techniques are array tomography  (4, 5) , SBF-SEM  (6) , and FIB-SEM  (7, 8). FIB-SEM can offer up to 4 nm isovoxel resolution covering entire cells  (9) , while SBF-SEM can cover a large field of view, up to entire multicellular or-ganisms  (10)  although compromising on slice thickness.
In general, volume SEM is used to capture a complete anatomic landscape at the nanometer scale, making it a suitable tool to study organelle morphology and organelle-organelle interactions. Thus, it can facilitate a better understanding of cellular processes, such as the dynamics of the endoplasmic reticulum (ER)  (11) , its contact sites to other organelles (with membrane distances of 20 to 100 nm)  (12, 13) , or viral replication factories driving viral production in infected cells  (14). After acquisition and image pre-processing, segmentation is the most common procedure to lay the foundation for quantitative morphometric analysis. By segmentation, we understand the grouping of an individual set of voxels into separated regions, following object boundaries within the dataset. In semantic segmentation, clusters of pixels are assigned to a specific label, e.
First, it requires an expert to generate a large corpus of annotated data, which will be used as ground truth during the training of the neural network. The network training process is often not very stable with a high risk of overfitting. Additionally, models for specific tasks usually do not generalize to new datasets (e. g. , due to variation in sample preparation, resolution, contrast/brightness). Since increasing the training and labeled data to have better coverage of the target domain is sometimes difficult or not possible, other strategies have been developed. In order to train models on the target domain, targeted or efficient ground truth annotation approaches are employed. For example, model-assisted labeling or active learning  (30) , uses a strategy of repeatedly annotating, training, and predicting a supervised model to iteratively converge to satisfactory results.
Within each iteration, the output is curated manually to generate a larger ground truth set for the next training step. If the dataset is challenging, this iteration process may prolong indefinitely, making it as arduous a task as manual labeling. Another way to increase the efficiency of ground truth annotation is the usage of semi-automated approaches  (13, 31). To further facilitate the training procedure and simplify setting up deep learning models, many approaches introduce pretrained network architectures  (28, 29)  or create readily implemented frameworks for training  (32) (33) (34) (35). Despite those efforts, training deep learning models effectively still requires a certain level of knowledge in machine learning, which might discourage non-experts from training with their own data.
Moreover, whereas organelle prediction maps generated by deep-learning-based approaches can yield high accuracies and enable the segmentation of a large variety of intra-cellular structures, none of the most recent organelle segmentation approaches circumvent the need for adapted post-processing efforts. With CebraEM, we introduce a machine-learning-based workflow, derived from the multicut pipeline  (20, 36)  that tackles the challenges mentioned above. CebraEM circumvents the need for training DL models entirely and generates automated semantic instance segmentations without the requirement for post-processing steps. The foundation of the CebraEM workflow is to split the segmentation task into first the prediction of organelle boundaries and then the detection of the target organelles. The prediction of boundaries is performed with a pretrained CNN model (CebraNET) which, without retraining, predicts organelle boundaries and enables the partitioning of the data into supervoxels.
The partitioning of the data reduces the complexity of the overall problem, enabling the usage of a shallow model as well as substantially increasing the speed of ground truth annotation. For the ground truth annotation of selected regions at the super-voxel level, we implemented CebraANN, a plugin for napari  (37). Once annotated and trained, the CebraEM inference workflow can generate a suitable semantic instance segmentation of the full cell within a few hours per organelle. To prove that our method can be generalized, we tested this segmentation workflow with several publicly available datasets covering a significant diversity in sample preparations method and volume SEM imaging modalities (see Methods, section Datasets): Firstly, we used a HeLa cell prepared by high-pressure freezing followed by freeze substitution and imaged by FIB-SEM (Hela-1) from the EM-PIAR database  (38)  which we segmented for the endoplasmic reticulum (ER), nuclear envelope (NE), mitochondria, and the Golgi apparatus.
A small subset of this dataset was used to train CebraNET. Secondly, we segmented a similar dataset, also containing a HeLa cell, also prepared by chemical fixation and freeze substitution and also acquired by FIB-SEM, which we used to prove the transferability of the method (Hela-2). Here, we segmented the ER, representing a fine-detailed organelle, as well as mitochondria, to show the quality when segmenting individual instances at a lower resolution. Thirdly, further transfer was shown with a dataset of a SARS-CoV-2-infected Calu3 cell which, in difference to the first two datasets, was chemically fixed and imaged by FIB-SEM (Calu3-3). We here segmented ER and mitochondria as well as the virus-induced double-membrane vesicles (DMVs). Furthermore, we used a dataset showing a macrophage that contained a parasitic vacuole.
The sample was, as the previous, prepared by chemical fixation and imaged by FIB-SEM (Macrophage-4). Here we focused our segmentation effort on the ER of the host as well as the parasitic ER. Finally, we segmented a dataset showing Platynereis dumerilii larvae prepared by chemical fixation and imaged by serial block-face SEM for mitochondria in ciliated cells. We thus proved the applicability to multicellular organisms and the scalability of our approach to datasets in the size scale of tera-bytes (Platynereis-5).
Results CebraEM is a workflow that combines an efficient segmentation step for the annotation of training data (Fig. 1a  and b ) followed by a fully automated shallow learning-based inference approach (Fig. 1c ). CebraEM's strengths articulate around three pillars, with the aim to make it accessible to a wide community of users: (I) it avoids the necessity to intensively train a neural network, (II) the inference model is trained on annotations of the same target dataset that the user chooses to segment, thus approaching the result to the specific domain of the data, and (III) it yields a fully learned semantic instance segmentation. From a user perspective, the CebraEM workflow starts with the initialization of a CebraEM project which can be visualized using the MoBIE viewer  (39). Using the viewer, the user navigates the dataset and selects suitable positions for ground truth cubes that contain the required target organelle(s) (Fig.