dataesr/scientific-paragraphs-categorization huggingface dataset:
a: OVERVIEW One of the most influential research work which addresses metadata completeness is Neumaier et al. [32]. They have assessed the metadata of about 260 open data portals towards the searchability, discoverability and usability of their resources. They developed a generic metadata quality assessment framework in which a set of quality metrics for metadata was proposed in line with the Data Catalog Vocabulary (DCAT) metadata standard. Authors map the dataset in various portals to its DCAT instantiation which can be expressed as a tree structure. The assessment metric has been evaluated by selecting an appropriate path in the DCAT instantiation and resolving the path to its value aggregated over the dataset. In particular, metadata completeness was interpreted as existence evaluation of metadata information in the research work.
b: PROBLEMS Open data platforms such as LOD cloud and governmental open data terminal are becoming widespread and important data source for research. Reference  [32]  mentioned that metadata quality issues in open data portals have been identified as one of the core problems for wider adoption of open data and developed a quality assessment and evolution monitoring framework for web-based data portal platforms, which offers their metadata in different and heterogeneous models. Similarly,  [41] ,  [66]  investigated the development of frameworks for assessing metadata quality in Knowledge Graph sources and  [38] ,  [71]  focused on assessing government open data platforms.
c: APPROACHES AND METRICS There are six articles that focus on metadata completeness  [32] ,  [38] ,  [40] ,  [41] ,  [66] ,  [71]. A lot of emphasis is currently VOLUME 9, 2021 placed on the completeness of the dataset itself only, but the importance of completeness of the metadata cannot be understated. Descriptive metadata about existing datasets are a substantial building block for facilitating entities and datasets linking, entity retrieval, distributed search or query federation. Ellefi et al. [41]  developed a framework for dataset profiling for the formal representation of a set of features that describes a dataset and allow the comparison of different datasets. They provided a taxonomy, formally represented as an RDF vocabulary of dataset profiling features. Furthermore,  [40]  presented an approach for capturing multilingualism as part of data quality dimensions, spanning completeness and  [38]  proposed a framework for comparison of open data portals for metadata quality using analytic hierarchy process.
7) LABELLING COMPLETENESS Labelling completeness is particular to RDF data, where URIs are used for identification but are not very suitable for indexing purposes and human readability. This warrants that entities have a human readable label and the level for which it is not missing is labelling completeness. For example, the existence of rdfs:label for instance of a city Paris. Definition 7 (Labelling Completeness): Labelling completeness refers to the degree to which entities in the dataset have a human readable label.
a: OVERVIEW According to Ell et al. [51] , even though it is assumed that labels for resources in a dataset will be made available, among other information, by dereferencing the URI of an entity using the HTTP protocol, following Linked Open Data principles. A whole lot of applications fall back to exposing the URIs of the entities to the user in the absence of more easily understandable representations such as human-readable labels. It is proposed that this is often due to issues such as internationalization, multiple labels for an entity, the computational costs associated with dereferencing, or the use of alternative labeling properties that makes the task of finding a label for a given entity much harder than expected. This work defines a number of metrics that provides a baseline for a quantitative analysis of the state of labeling on the Web.
b: PROBLEMS Reference  [51]  was focused on the internationalization of knowledge bases, existence of multiple labels for an entity, the computational costs associated with dereferencing URIs and proposed that all entities in a knowledge base have human readable labels. They also explored development of a metric for labelling completeness. Furthermore,  [54]  was interested in enterprise data integration and investigated the assessment of data quality for a Linked Enterprise Data in the automotive industry. The authors calculated the average number of resources that did not have a label property defined and stressed the need for it due to the heterogeneous nature of environments that generates different components of the dataset.
c: APPROACHES AND METRICS Non-information resources are abstract ideas represented in LD dataset that may possess URI but cannot be directly accessed or downloaded via the internet, such as person. The importance of labels for non-information resources in Knowledge Graph cannot be overstated since they provide appropriate context to understand the dataset. It helps indexing and searching the resources and displaying data to end-users that can be easily understood, rather than URIs  [51]. Only three articles have examined labelling completeness  [51] ,  [54] ,  [66]. According to Ell et al. [51] , labelling completeness measures the degree to which Linked Data resources have labels, it can be defined as the ratio of URIs with at least one value for a labeling property to all URIs in a given knowledge base.
2) LOUPE Loupe 8   [17]  is a tool that can be used to inspect a dataset to understand which vocabularies (classes and properties) are used with statistics and frequent triple patterns. Starting from the high-level statistics, Loupe allows one to zoom into details down to the corresponding triple with its visual explorer. It is a semi-automatic tool as metrics needs customization by the user. This can be used to assess schema and property completeness. Population completeness can also be assessed with external data.
3) LUZZU Luzzu 9  [28]  is also a Quality Assessment Framework for Linked Open datasets based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. It provides a library of generic quality metrics that users can customize based on domain-specific tasks in a scalable manner, thus it is semi-automatic; it also provides queryable quality metadata on the assessed datasets and assembles detailed quality reports on assessed datasets. It is useful for assessing property, schema, interlinking, metadata and labelling completeness albeit it requires manual configurations by the user.
4) LINK-QA Link-QA 10 [30] specifies a framework for detection of the quality of linksets using network metrics (degree, clustering coefficient, open sameAs chains, centrality, description richness through sameAs). It is completely automatic and compatible with a set of resources, SPARQL endpoints and/or dereferencable resources and a set of triples as input. It is particularly useful for assessing interlinking completeness. 9 https://eis-bonn. github. io/Luzzu/ 10 https://github. com/cgueret/LinkedData-QA.
8) LODQM LODQM  13   [55]  is an automatic tool developed around goalquestion-metric  [76]  approach to soliciting metrics used for assessment of datasets. It is suited for assessing schema and property completeness. 9) KBQ KBQ 14  [50]  is a tool geared towards assessment of quality of datasets based on temporal analysis. It automatically computes the frequency of predicates and the frequency of entities of a given resource type, and compares the frequencies with the ones observed in previous versions of the dataset. It can be specifically used to assess currency completeness.
C. MAINTENANCE OF DATA QUALITY After the assessment of data quality, the next step is to improve the quality taking into account the results from the assessment. This cycle of assessment and improvement should be done at regular intervals of time and/or when the data is updated. Additionally, a data quality issue in one 14 http://datascience. ismb. it/shiny/KBQ/ dataset can ultimately affect the quality of multiple interlinked datasets, thus propagating the errors. Consequently, maintenance of quality becomes challenging in the Web of Data mainly because it is generated from existing data and thus its correction can be even more difficult and time consuming when meta-information provenance is not available anymore  [77].
D. QUALITY-BASED QUESTION ANSWERING When existing linked datasets are published along with their quality information, it can be possible to design a new generation of quality-based question answer systems, which rely on this information to deliver useful and relevant results  [78]. In order to provide the answer to user queries in a meaningful way, it is necessary to define what should be in the result, how it can be obtained and how one should represent the query result. In this case, the completeness, consistency (logical/formal), timeliness, etc. of the data affects the results considerably. For example, querying an integrated dataset for a particular flight time, the time from the source with the higher update frequency and more complete information should be chosen. Thus, question answering can be increased in effectiveness and efficiency using data quality criteria as a leverage to filter the most relevant results.
E. STREAM-LINING FUTURE SURVEYS This SLR took eight months in total to be performed. In order to increase the efficiency and sustainability of such SLRs in the future, we propose (i) future surveys on Knowledge Graph and Linked Data quality, specifically on completeness, tag their articles with the type of completeness (listed in Section II-B) as keywords and (ii) we, as a community, think of combining human and machine effort towards streamlining such SLRs. We resonate with the idea proposed in  [79]  of living systematic reviews combining humans and machines. For some of the repetitive and labor-intensive tasks, machines can assist, such as, for searching relevant articles and eligibility analyzing. Then, humans can assist in extracting relevant information from within the text. Workflows can be developed in which human effort and machine automation can each enables the other to operate in more effective and efficient ways, offering substantial enhancements to the productivity of systematic reviews  [79].
IV. CONCLUSION AND FUTURE WORK The Linked Open Data principles are applied in various domains including life science, media, medicine and egovernment. All these areas require high quality of data since human lives are directly impacted; for example, IBM reported that poor quality data costs the US economy $3. 1 trillion dollars a year. 15 This raises the need for developing methods to evaluate and improve (Linked) data quality on the Web. This work focuses on completeness, which is one of the most important dimensions for Knowledge Graph quality assessment  [7]. In this article, we surveyed the research topic on completeness of Knowledge Graph. We analyzed 56 studies and classified seven types of completeness. We provided definitions for each type, identified the different kinds of problems that they address, provided approaches and metrics for assessment and analyzed the tools available for assessment of LD completeness.
Therefore, this may lead to the omission of interesting approaches that do not use these keywords. We already discussed the internal threat of validation due to open world assumption of LD in Section III. The future direction of our work entails the plan to expand our research by adding more relative keywords RDF dataset and RDF graph as RDF is the most common way of representing Knowledge Graph and a lot of quality assessment methodologies were performed relying on the properties of RDF. Moreover, we intend to develop our search strategy in order to cover types of completeness having alternative names such as schema and ontology completeness and include terms such as coverage as a synonym for completeness. Finally, we also intend to select other core dimensions like accuracy or timeliness of knowledge graphs and replicate the review process for in-depth analysis as a data quality dimension.
3 ) 3 Another purpose of Mendes et al. framework [61] , that was explained in Section II-B2, is measuring another type of completeness besides to property completeness. It takes into account the instantiations of properties in order to measure population completeness. In this work, the scoring function to compute population completeness is defined as:||obj. with property p in dataset || || all uniq. obj. in universe || (VOLUME 9, 2021.
Instruments are not ranked. -Narrow-angle, multi-color, visible imaging camera (heritage: Europa Clipper EIS) for observing geologic features at small scales (<1 km), acquiring geodetic images, stereo for local topography, and high-cadence image sequences (i. e. , movies) for monitoring active phenomena like plumes and eruptions. -Wide-angle, multi-color, visible imaging camera (heritage: Europa Clipper EIS, New Horizons MVIC) for mapping at regional and global scales, providing geologic context for high-resolution datasets, change detection, and global topography. determining the composition and isotopic abundances of Io's atmosphere and plumes, and Jupiter's magnetosphere. -Radio and gravity science (heritage: most planetary missions) for determining the gravity field of Io, astrometry, and atmospheric science.
The miniaturization of spacecraft has revolutionized the exploration of space 49. These small satellites (henceforth, "CubeSats," regardless of size or form) are enabled by significant technology maturation and cost savings due to growing commercial sector demand, resulting in the ability to build spacecraft with "off the shelf" components that are capable of addressing specific scientific objectives and/or augmenting the capabilities of larger missions. Swarms and constellations of CubeSats offer a unique capability to perform distributed science observations. NASA has flown two deep space CubeSats (MarCO 1 and 2, which provided telemetry for the landing of InSight at Mars), and many others are in development. CubeSats have the potential to address high-level science questions at Io. In particular, short-lived sacrificial CubeSats could be used to gather unique high-risk/reward datasets that may not be possible/wise/prudent for a larger, dedicated Io mission.
However, JWST will be unable to observe Io at wavelengths longer than about 10 microns because every instrument will over-saturate due to Io's volcanoes and scattered light from Jupiter. This will inhibit JWST's ability to perform a number of key investigations, like measuring Io's heat flow. The next generation of large ground-based telescopes (Giant Magellan Telescope, Thirty Meter Telescope, European Extremely Large Telescope) will have improved sensitivity and spatial resolution (by a factor of ~3) than current 8-10-meter class telescopes like Keck and the Large Binocular Telescope. The higher sensitivity and resolution mean that fainter atmospheric lines and volcanoes will be detectable, and it may be possible to observe active surface changes. Ongoing upgrades to the Atacama Large Millimeter/submillimeter Array (ALMA) interferometer are continually increasing the sensitivity and spatial resolution of the instrument, and thermal emission can now be mapped in the radio wavelengths at the same spatial scale as reflected sunlight in the optical and infrared, enabling powerful multi-wavelength datasets for exploring Io's surface.
Slopes were compared with the F test. The type I error was set at 5%. The analyses were carried out using GraphPad Prism v6. 01 software (GraphPad Software, Inc. , La Jolla, CA 92037 USA). Results. During the study period, 896 patients had at least two B 12 measurements with B 12 ≥ 1000 ng/l at T1, including 448 patients without any previously known solid cancer or other elevated-B 12 -related cause. A cancer was diagnosed between T1 and T2 in 39/448 patients. After excluding patients with other elevated-B 12 -related diseases diagnosed during follow-up (n = 5, all had chronic liver disease), and patients out of delays for T1 or T2 (n = 15), we included 19 patients for analyses. 9/19 patients received curative cancer treatment between T1 and T2 and constituted the Curative care group, while 10/19 patients received only supportive care and represented the Supportive care group (Table  I ).
Critical nozzles investigated In order to investigate the effect of roughness on   in the turbulent regime, a total of 9 cylindrical nozzles have been manufactured, dimensionally verified and calibrated to produce a database. The diameters  range from 5 mm to 10 mm with an intermediate at 7. 5 mm. These diameters have been adjusted so that the experiments carried out are compatible with Cesame's current range of nozzle diameters. Table  2  gives the measured diameters for each manufactured nozzle as well as the diffuser length, the manufacturing non-dimensional roughness range and the corresponding non-dimensional measured roughness in both the convergent part and the cylindrical part. The diameter was precisely measured at the position z/d = 1. 9 (cf. Fig. 1 ) at the end of the cylindrical part of the nozzle (HEXAGON™ measurements) with an uncertainty of 0.
4 in order to study the influence of the diffuser length. Surface roughness measurements were performed with a CMM (coordinate measuring machine) with a touching probe on the inner surface of each nozzle. The probe is a 4 mm diameter ruby sphere mounted on a 50 mm long arm. The measurement evaluates the surface state along a line in the nozzle, from the end of the convergent part to the beginning of the diffuser part  [3]. The measurement is limited by the movement constraints of the probe. In order to obtain more information about the surface, these measurements were reproduced on eight equally-distributed lines every 45 • in a radial position  [6]. Filtering can then be applied to the measurement data to evaluate the different deviations from the mean line. A profile filter separates the profile into long and short wavelength components.
Acknowledgments We thank our colleagues  Bodo Mickan  from the  PTB in Germany and Ernst von Lavante from Essen-Duisburg University  who provided their insight and expertise. We would like to express our sincere gratitude to the editor and the reviewers for their time and thoughtful comments which have been incorporated into the revised manuscript. This work was supported by  Cesame-Exadébit  and a  CIFRE fellowship  with the  Pprime Institute.
Reproducibility plays an essential part in the success of a bioinformatics project. Indeed, Reproducibility makes it possible to guarantee the validity of scientific results and to simplify the dissemination of projects. To help disseminate Reproducibility principles among bioinformatics students, engineers and scientists, we created the FAIR_Bioinfo course, which presents a set of features we consider necessary to make a complete bioinformatics analysis reproducible. To illustrate the theoretical concepts of reproducibility, we use as an example a classic bioinformatics analysis (differential gene expression analysis from RNA-seq data). In short, we retrieve the data from public databases (ENA/SRA), we perform a reproducible analysis using a workflow management system (snakemake) in a virtual environment (Docker).
How to use The whole example proposed in FAIR_Bioinfo can be simply reproduced with the following 4 command lines executed in a terminal (note that docker must be installed beforehand -https://www. docker. com/products/docker-desktop): $ git clone https://github. com/thomasdenecker/FAIR_Bioinfo $ cd FAIR_Bioinfo $ sudo docker run --rm -d -p 8888:8888 --name fair_bioinfo -v ${PWD}:/home/rstudio $ sudo docker exec -it fair_bioinfo bash. /FAIR_script. sh Once the analysis is complete, the web application can be launched with the following command: $ sudo docker exec -it fair_bioinfo bash. /FAIR_app. sh and used by copying http://localhost:8888/rstudio/p/4444/ into your browser. Note : The use of the sudo command is not necessary for Mac OS and Windows. The demonstrations of the course have been made on a Linux environment (Ubuntu 18.
Statement of Need Recent reanalyses of biology  (Baker, 2016)  and computer science  (Warren, 2015)  papers found a staggering ratio of non-reproducible results. Our daily experience as bioinformaticians indicate the same issues exist in our field. It is therefore essential that good practices are implemented to ensure data integrity and reproducibility of analytical results. This course was created originally at the request of colleagues who wanted to be trained in reproducibility practices in their own language (French). This course was born from the combination of our personal experiences in reproducibility acquired during our research work in Bioinformatics. We therefore created and delivered a set of training sessions in French, including a complete workflow and associated course materials available on Github (https://github.
Learning Objectives and Content In this FAIR_Bioinfo training, we present a stepwise protocol to ensure the reproducibility of Bioinformatics analysis and to guarantee identical results from the same data set and over time. For this reason, we extended the FAIR principles popular in the field of data management  (Wilkinson et al. , 2016)  to propose FAIR principles for bioinformatics workflows: -Findable: The tools used are references in their field (bowtie2, samtools, HTseq-counts, …) -Accessible: Codes, slides, docker image are online (Github & Dockerhub) -Interoperable: The different tools will communicate with each other (conda & snakemake) -Reusable / Reproducible: The workflow is saved in a file whose execution replays the entire analysis identically (Jupyter, Rmarkdown, …) Our protocol is composed of seven main steps which application gradually increases the level of reproducibility (Figure  1 ).
To illustrate FAIR principles for bioinformatics workflows, we used a classic RNA-seq data analysis workflow. Starting from raw data (FASTQ files), this analysis aims to identify genes that are differentially expressed between different conditions. The underlying biology is not detailed, as our focus is on the reproducibility aspects rather than the specific bioinformatics protocol or biological question. Eventually, the RNA-seq analysis protocol used as an example here could be replaced by any other type of bioinformatics analysis.
Conclusion and Perspectives We proposed a training course to help scientists improve their computational practice by ensuring reproducibility of bioinformatic analysis. The training course is divided into 8 sessions each introducing a core competence for reproducible analysis. We also address possible future developments (last session), including: -Installing all tools in a Conda environment itself residing in a Docker image. The goal is to isolate the installation layer  (Grüning et al. , 2018)  -Setting up a Virtual machine to run the Docker container. The goal is to stand the test of time by also setting the Operating System. -Implement continuous integration to ensure that code changes do not cause changes in results. Through the FAIR_Bioinfo training, we offer users a solution to make Bioinformatics analyses entirely reproducible.
Implementation and illustrative scenario The system is written in Java using JADE framework. Physical models of boilers and steam transport are simulated in MATLAB and integrated in Java. A special class, AugmentedAgent, was created to encompass basic functionalities needed by all agents in the system -address books, internal time tracking, role assignment. Its children are specific implementations for different technologies -boilers, energy storage, chemical plants, turbines, trading floors and broadcasters. Ontologies, protocols and other basic utility functions are used as decorators for instances of AugmentedAgent (see Fig. 7 ). Our platform allows us to model physical systems (boilers, heat storage,. ) using MATLAB components and market participants through decision-making algorithms.