dataesr/scientific-paragraphs-categorization huggingface dataset:
Within iCUPE we will also develop specific datasets focusing on selected geographical areas in northern latitudes. The data provision is open and Russian iCUPE collaborators have also provided data from the Russian Arctic, such as atmospheric mercury measurements at Amderma station, elemental and organic carbon over the north-western coast of the Kandalaksha Bay of the White Sea and micro-climatic features and Urban Heat Island intensity 880 in cities of Arctic region. Each of the data sets are promoted through "teasers" (www. atm. helsinki. fi/icupe/index. php/submitted-datasets), which provides the metadata information pertinent to the data sets. Majority of archived datasets (as products) are directly linked (and downloadable) and corresponding metadata information included. The University of Helsinki will take responsibility for long-term storage, accessibility, and maintenance.
The raw data will be hosted and maintained by the dataset providers. 885 Selected iCUPE datasets are also to be tested and integrated into several platforms. To facilitate and standardize access to data cloud-based online platforms, known as the Data and Information Access Services (DIAS), providing storage, centralized access and handling to data, and processing tools. The DIAS platforms (CREODIAS -creodias. eu; SOBLOO -sobloo. eu; MUNDI -mundiwebservices. com; ONDA -www. onda-dias. eu/cms; and WEkEO -www. wekeo. eu) allow users to explore, process, and download Copernicus data and information as well as to have the ability to process and combine with data from 890 other sources. It is also possible to develop and host new applications on such platforms. Other tested platforms for pre/postprocessing/analysis of iCUPE data include the Virtual Laboratory (vlab.
geodab. org) Google Earth Engine (earthengine. google. com), Polar Thematic Exploitation Platform (portal. polartep. io), Global Earth Observation System of Systems Portal (www. geoportal. org). We will continue to explore these options for the iCUPE data provision in the future. All final data products and proxies developed will follow interoperability and data sharing principles endorsed by  GEOSS. 895  We explore possibilities to maximize the use of new data products and advertising these to larger communities, such as in For selected topical data sets, some will be included in the University of Helsinki smart-SMEAR platform  (Junninen et al. 900 2009)  and disseminated via metadata catalogs to a wider audience. As a whole we need to utilize the full capacity of the multiplatform approach (in situ, satellites and models) to address the fate of pollutants in the Arctic.
The basis of the analysis includes the observation capacity. Earth Observation (EO) inputs like SYNOP station measurements of physical atmosphere and in other stations, ocean variables, were linked to key products / outcomes / services such as numerical weather prediction and through groups like in this case weather service connected to key objectives of the assessment framework (Fig. 30 ), such as weather service or environmental information service. 915 As the observational capacity and its monetary value, we utilized the WMO OSCAR database for satellite and surface observation systems (https://www. wmo-sat. info/oscar/) north of 60° N. This provided us the volume of stations in different station and mission categories in the Arctic (Table  2 ). Based on our analysis, the total yearly value of this observation system 920 including EO inputs and modeling is over 204 million €.
The circumpolar coverage of in situ atmospheric observations has developed during the last decades. In selected locations, 960 such as Svalbard, observations are available for assessing the decadal variability of key observables, such as black carbon. The geographical coverage should be particularly expanded in the Russian Arctic  (Petäjä et al. , 2019)  although a lot of observation sites are operated already  (Alekseychik et al. , 2016). However, the connection of various continuous national activities and intensive campaigns within the Arctic should be coordinated in order to reduce operation costs. The observations should take advantage of data harmonization procedures set up e. g. by WMO-GAW and European Research infrastructures  (ICOS, 965 ACTRIS, eLTER). Work towards open data sharing should be continued.
Long-term observations of atmospheric mercury in the Russian Arctic were found to be connected with the distance to the sea. The modeling results underline the interconnected life-cycle between mercury halogen compounds and atmospheric 990 oxidants. Furthermore, the remote sensing activities in the iCUPE project highlighted the need of a ground truth concerning the fractional snow cover. This kind of information can be obtained by terrestrial photography and it represents a tool in common between remotely-sensed products useful for integrating satellite data with different spatial and time resolutions. This approach can be very effective in high-latitude areas where illuminating conditions, topography and cloud cover limit the use of optical remote 995 sensing. The satellite remote sensing work within iCUPE revealed that imaging spectroscopy also known as hyperspectral remote sensing for Arctic vegetation provides valuable information on vegetation status and biomass that can improve our https://doi.
org/10. 5194/acp-2019-1217 Preprint. Discussion started: 3 March 2020 c Author(s) 2020. CC BY 4. 0 License. understanding of long-term vegetation trends derived from broadband data. Spectral information stored in the narrow wavelengths of the visible spectrum are the most promising for differentiating spectrally similar vegetation communities and 1000 for delivering information on photosynthetic activity and biomass. The lack of ground-validated datasets and an overall scarcity of dense, high quality image time series remains a challenge. The increasing availability of hyperspectral data will face similar challenges and given the limited number of hyperspectral remote sensing studies in the Arctic, high quality, ground-validated data is required to accurately interpret these dense and complex datasets.
The rise of more advanced classification methods such as machine-learning techniques are highly promising for Arctic vegetation mapping using advanced remote sensing 1005 platforms. An increased effort to develop Arctic-specific algorithms is needed. Within iCUPE, the benefits of combining in-situ and satellite remote sensing and multiscale modeling in cryospheric, terrestrial and marine regimes as well as atmospheric domain are clear. Such integrative activities need to be expanded and continued in order to provide verified environmental data and services in the changing Arctic.
in air from the Arctic Ground based measurements for particle number, black carbon mass and ozone concentration Black carbon and aerosol absorption of Arctic research infrastructures Anthropogenic contaminants in snow from polar regions Anthropogenic contaminants in ice cores Emerging organic contaminants in snow from the Arctic Emerging organic contaminants in water from the Arctic Snow spectral reflectance Aerosol vertical profiles from ground-based and satellite observations in Finland and Russia Arctic atmospheric mercury observations: updated GMOS database Blueprint for novel proxy variables integrating in-situ and satellite data Arctic parameters based on ground-based remote sensing and airborne platforms Precipitation in the high-latitudes Novel optical remote sensing products on snow and on vegetation and gas flaring mapping in selected sites Arctic atmospheric mercury isotope observations Time series of lakes' size changes in Northeast Greenland Aerosol reanalysis for SMEAR-II Organic aerosols in the Arctic based on source apportionment Datasets resulted from collaboration with PEEX Russian partners Mercury measurements at Amderma station of the Russian Arctic Elemental and organic carbon over the northwestern coast of the Kandalaksha Bay of the White Sea Micro-climatic features and Urban Heat Island Intensity in cities of the Arctic region Atmospheric composition at Fonovaya Observatory, West Siberia 1800.
Acknowledgements  1010  This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  No 689443 via project iCUPE (Integrative and Comprehensive Understanding on Polar Environments )" and through  ACTRIS2  is gratefully acknowledged. Additional financial support through  Academy of Finland  ( Center of Excellence in Atmospheric Sciences ) and through  Academy of Finland  project  NANOBIOMASS  are gratefully acknowledged. P. O. thanks for partial support from project  Russian Foundation for Basic Research (RFBR ) No. 18-05-60084. Part of this work was 1015 supported in part by  Ministry of Science of Education of RF  (Agreement No. 14. 616. 21. 0104 , unique identifier RFMEFI61618X0104). We thank  J. Pelon  for assistance with research conducted at LATMOS and IGE.
Extracting top-k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, computation errors are introduced when analyzing only subsets of the dataset. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of top-k keywords and documents, it is customary to design benchmarks that compare weighting schemes within various configurations of distributed frameworks and database management systems.
Thus, we propose a generic document-oriented benchmark for storing textual data and constructing weighting schemes (TextBenDS). Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top-k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB proves to have the best overall performance, while Spark's execution time remains almost the same, regardless of the weighting schemes.
Considering this context, choosing a processing framework is not easy. Thus, benchmarking is usually used to compare combinations of weighting schemes, computing strategies and physical implementations. However, most big data benchmarks focus on MapReduce operations and do not specifically target textual data  [19, 43, 50]. Moreover, the few benchmarks that do feature textual data  [12, 13]  are still at the methodology or specification stages. T 2 K 2 D 2 (Twitter Top-k Keywords and Documents multidimensional) benchmark  [47]  solves some of the current issues by compare the processing of different weighting schemes on various relational and NoSQL database management systems (DBMSs). However, one of the major drawbacks of this previous solution is the fact that is design to work on a single node, as opposed to a distributed environment, which limits its work with large sets of data.
In this paper, we seamlessly expand and generalize T 2 K 2 D 2 to distributed systems to boost its scalability. For our proposed benchmark solution (TextBenDS), we considered a series of objectives, as follows. We aim to dynamically compute the weighting score at the execution of each new request on the dataset, thus improving the classical information systems' approach and solving the reproducibility problems  [26]  of computing the weight one time  [9, 4]. Therefore, when weights are computed, we will take into account the varying size and content changes upon the dataset that happens with time  [5]. Another objective is to redesign the schema to integrate new relationships to store metadata extracted during the preprocessing of the text, i. e. , tags and name entities. Finally, we are aiming at doing extensive experiments and successfully compare several big data processing solutions.
For this paper we will consider the following distributed solutions: Hive -a distributed DBMS  [42] , Spark -a distributed framework  [53]  and MongoDB -a document-oriented DBMS. As a general criterion, TextBenDS should be designed with Jim Gray's criteria for a "good" benchmark (relevance, portability, simplicity and scalability)  [16]  in mind. The remainder of this paper is organized as follows. In Section 2, we present a survey on existing big data and, more specifically, text processing-oriented benchmarks, also specifying what our solution brings in addition to the ones presented in this section. In Section 3, we describe the general specifications of TextBenDS, focusing in particular on the employed data and workload models and on the applied performance metrics. A detailed description of our distributed implementation can be found in Section 4.
BigBench  [15]  is the first benchmark that added semi-structured and unstructured data to TPC-DS  [44]  and was extended to work on Hadoop and Hive by implementing queries using HiveQL  [8]. Another improvement to this benchmark, that added additional queries, is BigBench V2  [14]. Although, the BigBench benchmark, and its extensions, are developed to work with multidimensional models and semistructured and unstructured date, their models do not take into account textual data and complex aggregation queries, e. g. queries that compute word weights dynamically or score documents using ranking functions. Similarly to BigBench, HiBench  [19]  is a micro-benchmark developed specifically to stress test the capabilities of Hadoop (both MapReduce and HDFS). Using a set of predefined Hadoop programs, ranging from data sorting to clustering, HiBench is measuring metrics such as response time, HDFS bandwidth consumption and data access patterns.
Another Big Data benchmark is MRBS  [36]. This solution provides workloads of five different domains with the focus on evaluating the dependability of MapReduce systems. SparkBench  [1, 25]  is a micro-benchmark suite developed specifically to stress test the capabilities of Spark on Machine Learning and Graph Computation tasks, rather that text preprocessing and computing weighting schemes. Moreover, Facebook developed LinkBench  [3]  to emulate social graph workload on top of databases such as MySQL. BigDataBench  [50]  features application scenarios from search engines, i. e. , the application on Wikipedia entries of operators such as Grep or WordCount. Yet, although BigDataBench is open source, it is quite complex and difficult to extend, especially to test the computation efficiency of term weighting schemes.
Either term weights are known before the algorithm is applied, or their computation is incorporated with preprocessing. Furthermore, none of these benchmarks propose adequate data sampling methods based on analysis requirements. BDGS  [28]  is a benchmark that generates synthetic big data datasets preserving the 4V Big Data properties. BDGS covers three representative data types (structured, semistructured and unstructured) and three data sources (text, graph, and table data). Although, BDGS generates textual data, the workloads only employ simple computations such as sort, grep and word count. There are other benchmarks that focus on the same workloads  [20]. The evaluation of complex computation, such as term weighting schemes, are not taken into account by any of these benchmarks. FakeNewsNet  [37]  is a text repository of news content, social context and dynamic information for benchmarking fake news detection, diffusion, and mitigation.
The features it presents are linguistic, user profile data, and social network context. The benchmark does not propose any data sampling methods or weighting scheme computation of terms or documents. Furthermore, aggregation methods can be used to combine different features representations into a weighted form and optimize the feature weights by using weighting schemes in the case of textual data  [38]. Thus, the existing text analysis benchmarks do not evaluate weighting schemes construction efficiency. This is why we introduced T 2 K 2  [46] , a top-k keywords and documents benchmark, and its decision support-oriented evolution T 2 K 2 D 2  [47]. Both benchmarks feature a real tweet dataset use case and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance.
Yet, these solutions are not tailored for distributed computing. As a conclusion to this section, our benchmark (TextBenDS) addresses the following shortcomings that exist in the current literature: i) proposes a number of aggregation queries to compute term weighting schemes and document ranking functions; ii) tests the computation efficiency of term weighting schemes; iii) offers adequate data sampling methods for analysis; iv) works with structured, semi-structured, and unstructured data in the form of textual data; v) enables analysis based on gender, location, and time to extract general linguistic and social context features.
Generic Data Model For TextBenDS, we remodeled the T 2 K 2 D 2 multidimensional schema to incorporate new information about the textual documents, thus changing the logical model from a star schema to a snowflake schema. We kept the central fact tables that stores information about the documents, but added new entities to store the tags and named entities. TextBenDS's multidimensional snowflake schema is presented in Figure  1. The models' entities are briefly presented below. -DocumentFacts is the central fact entity and contains the number of co-occurrence f t,d and term frequency T F(t, d) for a lemma in a document. The information stored in this table is used for computing the weighting schemes and the ranking functions for extracting the top-k keywords and documents. -DocumentDimension is the document dimension table, containing each document's unique identifier and the original and processed text, i.
e. , original text (RawText), clean text (CleanText) and lemma text(LemmaText). After the top-k documents search process is finished, the information in this entity is used to better visualize the results. -WordDimension stores the word's lemma and its unique identifier. The information stored in this entity is a central part of the two tasks at hand. It is used to correlate the top ranking terms with the weighting for the the task of top-k keywords. When searching for documents that match specific search queries, a filtering process is applied on the word attribute to select only the documents that contain the search terms before computing the scores for the ranking functions. -TimeDimension stores the full date and also its hierarchy composed of minute, hour, day, month and year. By filtering this dimension, analysts can apply the roll-up and drill-down operations to better understand the textual data from a time perspective and create different text cubes.
Likewise, time series analysis can be applied here, in order to better [understand|interpret] the information. -AuthorDimension stores information about an author's unique identifier, gender, age, firstname, and lastname. By adding constraints on this dimension, analysts can select targeted genders and age ranges for the data mining process. -LocationDimension stores the geo-location coordinates for each document. This information is used in filtering to extract only areas of interest for the analysis process. -NamedEntityDimension stores named entities that extracted after corpus preprocessing. These entities consist of real-world actors, such as persons, locations, organizations, products, etc. By adding filtering constraints on this entity, the analysis process can target named entities of interest, thus improving the decision making process offered by business intelligence techniques.
-TagDimension stores the labels for documents, i. e. tags. These tags can be original documents' labels or metadata extracted through preprocessing, such as social media tags. Social media tags include hashtags and mentioning tags, which consists of citing other users' screen names in tweets (using the syntax @username). Hashtags can be used in filtering the dataset based on user interests, while mentioning tags are useful in data mining and graph mining for detecting leaders and followers and to construct the interactive graphs. For each dimension, we chose members that best represent a generic model of textual data. However, the model can be adapted to specific datasets, maybe containing other kinds of textual information. The workload model for top-k keywords follows three major analysis directions by subsampling the corpus using filters on gender, time, and location.
Also, we further the analysis by extracting top-k documents and subsampling the dataset by adding filters on the WordDimesion entity. The workload model uses OLAP queries to extract information from the multidimensional data model. We use gender-based filters to extract two sets of features. The first set consist of general linguistic-based features. Using these features, analysts can detect different patterns in writing styles and construct gender-based vocabularies. The second set contains social context features and it's specific to social media datasets. The analysis process based on these features extracts information about gender-based events and objects of interest, as well as psychological profiles and social engagements between authors of the same or of different genders. We use time-based filters to detect changes that appear over time in the vocabulary, thus extracting topics and events.
There are many constrains combinations for the filter categories discussed above. But, we consider that the most representative constrains for detecting the vocabulary, user behavior, social context, events and opinions are the following: i) for top-k keywordsc 1 , c 1 ∧ c 2 , c 1 ∧ c 3 , and c 1 ∧ c 2 ∧ c 3 , and ii) for top-k documents -c 1 ∧ c 4 , c 1 ∧ c 2 ∧ c 4 , c 1 ∧ c 3 ∧ c 4 , and c 1 ∧ c 2 ∧ c 3 ∧ c 4. Eventually, let us emphasize that, in our workload model, filter values are given at execution time and the scores for extracting the top-k keywords and documents are computed in near-real-time. Our model can handle dataset modifications since weights are computed dynamically, therefore removing possible computation errors. This is an improvement over current information retrieval systems that compute weights only once, when the information is loaded in the database, thus incorporating errors if the ranking the data is modified throught inserts, updates or deletes.
Weighting Schemes A weighting scheme is used in Information Retrieval and Text mining as a statistical measure to evaluate how important a term is to a document in a collection or corpus. The importance increases proportionally to the number of times a term appears in the document but is offset by the frequency of the term in the corpus. We employ two weighting scheme techniques. The first is the term frequency-inverse document frequency (TF-IDF) weighting scheme. It is often used as a central tool for scoring and ranking terms in a document. Given a corpus of documents D = {d 1 , d 2 ,. , d N }, where N = |D| is the total number of documents in the dataset and n the number of documents where some term t appears. The TF-IDF weight is computed by multiplying the augmented term frequency T F(t, d) = K + (1 -K) • ft,d max t ∈d ( f t ,d ) ) by the inverse document frequency IDF(t, D) = 1 + log N n , i.