dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, the outbreak of severe acute respiratory syndrome associated Coronavirus (SARS-CoV) in 2003  5  , the 2009-2010 influenza A(H1N1) pandemic  6  , the Middle East respiratory syndrome Coronavirus (MERS-CoV) outbreak in 2012  7  , the 2013-2016 west African Ebola virus disease epidemic  8  , and the 2015-2016 zika virus epidemic  9  ; all spread in several countries across different continents, disproportionately impacting the most vulnerable communities  10, 11. The increasing number of disease outbreaks has also lent impetus to a growing research interest on examining this phenomena  12. However, this has gone hand-in-hand with a rising need for reliable open data from official sources. Existing datasets on the matter exclusively cover a limited number of infectious diseases  [13] [14] [15]  , or are specific to a population, country, or region  [16] [17] [18].
Moreover, other datasets are based on unofficial information  19, 20  , which may contain incorrect information or disinformation from false reports, or are not publicly available  21  , hampering their reuse and utilization. The WHO, as part of its mandate, collects information about confirmed and potential public health events of concern in the world. Specifically for COVID-19, the WHO made available the Coronavirus Dashboard 1. For the rest of diseases, the information is contained in the Disease Outbreak News (DONs)  22. The information of the DONs is obtained from an integrated global system coordinated by the WHO. This information is based on epidemiological, clinical, and laboratory investigations conducted by the official public health authorities, institutions, and research networks of the WHO and its partners all over the world.
However, since the DONs are not primary produced for statistical purposes, they are unstructured, with a format that makes it difficult to extract detailed information, and they do not make use of concepts and definitions that conform to international standards. Therefore, to create a database that is statistically sound for research purposes, we first processed the information from the DONs and then merged it with the Coronavirus Dashboard data. This data creation process is described in the Methods section. Our final dataset contains information on 2227 disease outbreaks which occurred over the period from January 1996 to March 2022. In comparison with existing data on the matter  [13] [14] [15] [16] [17] [18] [19] [20] [21]  , our dataset provides five key advantages. First, a wide geographic coverage of 233 countries and territories around the world.
Second, an extensive coverage of 70 infection diseases. Third, the utilization of standardized concepts and definitions, for which we used the codes of the International Standard Organization for countries and territories (ISO-3166)  23  , and the tenth revision of the International Statistical Classification of Diseases and related Health Problems (ICD-10)  24. This allows the users to flexibly obtain information for a specific region, country, year, or disease. Fourth, for transparency, replicability, and reproducibility purposes, we make the data, metadata, and the code to create these data publicly available from Figshare  25  both in human-and machine-readable formats (HTML, R, csv), which facilitates the reuse of the information. Moreover, by re-running the code the user can automatically extract more recent DONs from the website to keep the database updated.
Finally, our data are interoperable, i. e. they can be easily integrated with other data by using the country code, the year, and/or the disease code as key variable(s) matching observations between datasets. To illustrate the potential use of our dataset to the research community we apply an exploratory spatial data analysis (ESDA) in the Usage Notes. Our results show that outbreaks tend to be spatially clustered. We find evidence of the existence of spatial clusters of high incidences ("hot spots") in Africa, America, and Asia. This spatial analysis enables policymakers to identify the countries with the greatest likelihood of suffering from disease outbreaks and, taking into account their degree of preparedness  26  and vulnerability  27  , to develop policies that may help contain the spreading of future outbreaks  28.
• A known cause with a demonstrated ability to produce a serious public health impact and spread internationally. • High public concern potentially leading to disruption of required public health interventions or could disrupt international travel or trade. Given the relevance of COVID-19, the WHO developed the Coronavirus Dashboard to provide official daily counts of COVID-19 cases, deaths, and vaccines 1. The Coronavirus Dashboard is available at https://covid19. who. int/ and presents information as reported by official public health authorities of the countries and territories in the world. Data collection and integration processes. Figure  1  provides a schematic overview of the data collection and integration processes to create our dataset. These processes are described in detail below.
The code to replicate all these steps, from web scraping to the creation of the final dataset, is found in the file "DONs. R". This code, together with the databases have been made available from Figshare  25. The permission to extract all the information from the WHO webpage and redistribute the resulting dataset under an open data license assigned to our Figshare was authorized under request 388191 submitted through WHO's online platform. In stage (A) of Fig. 1 , we collected 2721 DONs from the WHO webpage. This collection was carried out by developing a code in R to automatically extract the information of the DONs. The oldest DON is related to cholera outbreaks reported on 22 January 1996 in Cabo Verde, Côte d'Ivoire, the Islamic Republic of Iran, Iraq, and Senegal. The most recent DON that is covered in our dataset refers to an outbreak of yellow fever in Kenya and was registered on 25 March 2022.
To this dataset we added a column to uniquely identify each of the DONs. See file "DONsRaw. RData" for a raw dataset of the 2721 DONs. Once the 2721 DONs were collected, we subset them to exclusively include those events related to the occurrence of disease outbreaks. In particular, we deleted DONs reporting other types of events, such as meetings of international experts (see DON0133), recommendations on treatments (DON0153), or travel requirements (DON0107 and DON0198). To ensure the utilization of standardized concepts and definitions, we used the official short country names in English according to the ISO-3166  23  , and the ICD-10  31. The resulting dataset has still three recording issues that need to be tackled. First, some observations refer to multiple outbreaks. Second, some DONs inform about disease outbreaks happening in more than one country.
For variants or mutations of viruses, as in the case of the avian influenza and its variants A(H1N1), A(H1N2), A(H5N1), A(H3N2), among others, all of them are considered as the same disease, i. e. influenza due to identified zoonotic or pandemic influenza virus. In this way, we ensured to have only one observation per disease, country and year. In other words, our unit of analysis in the database is an outbreak that happens when a country has at least one case of a specific disease during a particular year. Thus, a specific country cannot have two outbreaks related to the same disease in the same year but can have more than one outbreak of different diseases in the same year. Moreover, a country can only have more than one outbreak of the same disease if and only if they refer to different years.
The resulting dataset consists of 1562 observations with unique outbreaks, covering a total of 217 countries and territories, and 69 different infectious diseases occurred over the period from January 1996 to March 2022. See file "UniqueDONs. RData". Since outbreaks related to COVID-19 are not included in the dataset of the DONs, we proceed to extract this information from the Coronavirus Dashboard, as shown in stage (B) of Fig. 1. In particular, we dichotomized the information on the cases per country per year to assign a one if the country had at least one case of Coronavirus, and zero otherwise. For standardization purposes, we used as in stage (A) the official short country names in English by the ISO-3166  23   For the spatial analysis shown in the Usage Notes, we used in stage C) the dataset "World Administrative Boundaries -Countries and Territories" 32 by the Société OPENDATASOFT available at https://public.
opendatasoft. com/explore/dataset/world-administrative-boundaries/export/. This dataset is a shapefile with geographic information about the administrative boundaries of countries as well as non-sovereign territories and is published under the Open Government Licence v3. 0. We added this geo-spatial information to the dataset with 2227 unique disease outbreaks by using as key variable the alpha-3 country code from the ISO 3166. See the code to create this dataset and replicate the analysis in file "ESDAoutbreaks. R" and the final dataset in file "GeoOutbreaks. RData". Data management. To enhance the transparency, reproducibility, and reusability of our datasets for scientific purposes we follow the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles for scientific data management and stewardship  33.
In particular, we addressed these principles by adding the following attributes to our database. First, we assigned a Digital Object Identifier (DOI) from Figshare  25. This DOI is a globally unique and persistent identifier that makes the data findable. Second, in order to make the information accessible, this DOI is linked to a repository where users can freely retrieve the data files, documentation, and code. Third, to make the datasets interoperable we use terminologies and vocabularies that are commonly used in the field of epidemiology by using the ICD-10 31 and the ISO-3166  23. To document the database, metadata on Figshare uses the Fields of Research schema. Moreover, the datasets are available both in human-and machine-readable formats (HTML, R, csv) and thus, users can easily interact with the resources.
technical Validation The process of technical validation is described in a schematic form in Fig. 2. In step (A) we uploaded the final dataset in R. From it, we randomly selected a sample of 100 observations from the 2227 disease outbreaks (B). Then, we used the information in the column DONs, that includes the unique identifier of the DONs reporting that outbreak, to manually check the information by accessing to the corresponding link of the WHO where the DON is described (C). Three attributes were observed: information about the country, disease, and year. After validating all the information (D), we obtained a 98% of correctness for the information about the country (we initially wrote "Tanzania", instead of "Tanzania United Republic of ", and "Moldova", instead of "Moldova Republic of "), 99% for disease (we erroneously wrote in column icd104n "Dengue fever, unspecified", instead of "Dengue, unspecified"), and 100% for year (E).
Usage Notes The format of our dataset allows the users to flexibly extract and manipulate the information, as well as put it together with other datasets when using the country code from the ISO-3166 or the disease code from the ICD-10. Target applications of the database presented in this paper should focus on combining our datasets with variables from other sources to study, for instance, the link between spatial, temporal, environmental, globalization, or socioeconomic factors with the exposure of countries to disease outbreaks. Moreover, researchers may be interested in extracting a subset of data to analyze a specific country or disease and integrate it with related country-specific or disease-specific information to describe more in detail the outbreaks. As an example, we apply an exploratory spatial data analysis (ESDA) to the total frequency of outbreaks by country (or territory) between 1996 and 2021 (2022 is not included, given that only the first three months of the year are available).
The code to create the dataset with geographic information and replicate this analysis can be found in the file "ESDAOutbreaks. R". empirical strategy. The goal of the ESDA applied to our data is to detect significant spatial patterns in disease outbreaks. The methodology used consists of the following three steps: 1. Global Moran's I estimation. Under the null hypothesis that our recorded disease outbreaks are randomly distributed in space, this method evaluates the Moran's I statistic for spatial autocorrelation, i. e. the correlation between the frequency of outbreaks across the geographical space. The formula to calculate the Moran's I statistics is expressed as follows: I n x x x x x x ( )( ) ( ) w i j w i 2 ij ij ∑ ∑ ∑ = ∑ - - ∑ - where x i , x j are the observed frequency of outbreaks for countries (or territories) i, j; x is the average of the frequency of outbreaks over the n countries (or territories); w ij denotes spatial weights.
Information about the geographical distribution of disease outbreaks may also be essential for national governments to advice individuals on whether to vaccinate before travel, for example. It can also help public health authorities to improve bio-surveillance and act for a better long-term preparedness in the most exposed countries to disease outbreaks. Although the database presented significantly contributes to the study of global patterns in disease outbreaks, it has some limitations. We caution that studies using our data should be aware of two main issues when interpreting their results. First, information exclusively captures the occurrence of an outbreak associated to a particular disease during a given year in a country and not the intensity. This is an important contribution but does not reflect other relevant aspects in the study of epidemics, such as the number of cases or deaths associated to the outbreak, which are not available in the DONs.
, and the ICD-10 31. The resulting dataset of Coronavirus outbreaks consists of 665 observations which occurred since 2020 and until 31 st March 2022, covering a total of 224 countries and territories. See file "COVIDOutbreaks. RData". The standardization of the country names allows us to use the country code as a key variable to merge the data of Coronavirus outbreaks with the outbreaks' dataset of the DONs. The resulting data from merging these two datasets consist of 2227 observations of unique outbreaks of 70 different infectious diseases that occurred in a total of 233 countries and territories from 1996 until March 2022. See file "Outbreaks. RData".
WHO), Global database, Exploratory spatial data analysis (ESDA). Description: R-code to replicate the exploratory spatial data analysis (ESDA). 7. " GeoOutbreaks. RData" Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database, Geo-spatial information. Description: Dataset containing the total number of unique disease outbreaks per country with geo-spatial information. Data records: "iso3": Alpha-3 country code from the ISO 3166. "region": Region of the country. "continent": Continent of the country. "name": Name of the country where the outbreak occurred. "french_shor": Name of the country in French. "freq": Total frequency of disease outbreaks. "geometry": Geographic coordinates of the country.
Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database, International Statistical Classification of Diseases and related Health Problems, ISO 3166. Description: R-code to replicate the extraction of data from the Disease Outbreak News (DONs), available at www. who. int/emergencies/disease-outbreak-news, and the Coronavirus Dashboard, available at https:// covid19. who. int/, produced by the World Health Organization (WHO). In addition, this code includes the integration, data correction, and use of standard names and definitions from the ISO 3166 and the International Statistical Classification of Diseases and related Health Problems. 2. "DONsRaw. RData" (or alternatively "DONsRaw. csv) Dataset containing 1562 observations with unique outbreaks, covering a total of 217 countries and territories, and 69 different infectious diseases.
"icd11l1": Name of the type of disease according to the ICD-11. "icd11l2": Name of the subtype of disease according to the ICD-11. "icd11l3": Name of the disease according to the ICD-11. "Disease": Name of the disease. "DONs": Coronavirus Dashboard. "Definition": Definition of the disease according to the ICD-11. 5. "Outbreaks. RData" (or alternatively "Outbreaks. csv") Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database. Description: Dataset containing 2227 observations (unique disease outbreaks), occurred in a total of 233 countries and territories from 1996 and until March 2022, and associated to 70 different infectious diseases. A unique outbreak happens when a country has at least one case of a specific disease during a given year.
( WHO), Global database, Exploratory spatial data analysis (ESDA). Description: R-code to replicate the exploratory spatial data analysis (ESDA). 7. " GeoOutbreaks. RData" Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database, Geo-spatial information. Description: Dataset containing the total number of unique disease outbreaks per country with geo-spatial information. Data records: "iso3": Alpha-3 country code from the ISO 3166. "region": Region of the country. "continent": Continent of the country. "name": Name of the country where the outbreak occurred. "french_shor": Name of the country in French. "freq": Total frequency of disease outbreaks. "geometry": Geographic coordinates of the country.
A unique outbreak happens when a country has at least one case of a specific disease during a given year. Thus, a specific country cannot have two outbreaks related to the same disease in the same year (but can have more than one outbreak of different diseases in the same year). Moreover, a country can only have more than one outbreaks of the same disease, if and only if they refer to different years. 1. "DONs. R" Categories: Statistics, Epidemiology, Diseases Item type: Software Keywords: 3. "UniqueDONs. RData" (or alternatively "UniqueDONs. csv") Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database. "icd11l2": Name of the subtype of disease according to the ICD-11.
"icd11l3": Name of the disease according to the ICD-11. "Disease": Name of the disease. "DONs": List of DONs reporting the outbreak. "Definition": Definition of the disease according to the ICD-11. 4. "COVIDOutbreaks. RData" (or alternatively "COVIDOutbreaks. csv") Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database. Description: Dataset containing 665 observations occurred since 2020 and until March 2022, covering a total of 224 countries and territories in which cases of COVID-19 were reported. In particular, we dichoto-mized the information on the cases per country per year to assign a one if the country had at least one case Description: "icd11l1": Name of the type of disease according to the ICD-11.
of Coronavirus, and zero otherwise. Categories: Statistics, Epidemiology, Diseases Item type: Dataset Keywords: Disease Outbreak News (DONs), the Coronavirus Dashboard, World Health Organization (WHO), Global database. Description: Dataset containing the raw data from 2721 DONs as extracted from www. who. int/emergencies/disease-outbreak-news. The dataset presents information from 1996 to March 2022 (last DON was registered on 25 March 2022). Data records: "ID": DON unique identifier. "Description": Name of the DON as extracted from www. who. int/emergencies/disease-outbreak-news. "Date": Date of registry of the DON. "Link": Link to the website to find more information on the DON.
Introduction The era of "Big Data", which deals with massive datasets, has brought new analysis techniques for discovering new valuable information hidden in the data  [1]. Among these techniques is multilinear low-rank approximation (LRA) of matrices and tensors, which has recently attracted much attention from engineers and researchers in the signal processing and machine learning communities  [2]. A tensor is a multidimensional array and provides a natural representation of multivariate and high-dimensional data. Low-rank approximation of tensors (t-LRA) can be considered as a multiway extension of LRA of matrices (which are two-way) to higher dimensions  [3]. Generally, t-LRA is referred to as tensor decomposition which allows factorizing a tensor into a sequence of basic components  [3].
As a result, t-LRA provides a useful tool for dealing with several large-scale multidimensional problems in modern data analysis which would be, otherwise, intractable by classical methods. Two widely-used approaches for t-LRA are CP/PARAFAC decomposition 1   [4]  and Tucker decomposition  [5]. Under the CP format, a tensor can be represented as a sum of rank-1 tensors; each rank-1 tensor is formulated as the outer product of vectors. Under the Tucker format, a tensor is factorized into a sequence of factor matrices acting on a reduced-size core tensor. "Workhorse" algorithms are based on the method of alternating least-squares (ALS). The readers are referred to the work of  [3]  for a good review. Characteristics of big data are associated with the following three "V"s: high volume, high velocity and high veracity  [1].
Thanks to the recursive least-squares technique in adaptive filtering and alternating direction method of multipliers (ADMM) method, RACP is capable of online detecting sparse outliers and tracking successfully the underlying CP model of data streams over time. In parallel, some adaptive CP algorithms, such as  [15] [16] [17] [18] , are capable of handling higher-order tensors. However, they do not handle incomplete datasets. For adaptive Tucker decomposition, Kasai et al. introduced RPTucker  [19] , dealing with dynamic tensor completion. Leveraging a specific Riemannian metric, RPTucker effectively performs preconditioned SGD on the Riemannian manifold of the subspace spanned by tensor factors. Very recently, Gilman and Balzano proposed TOUCAN (tensor rank-one update on the complex Grassmannian)  [20] , for tensor singular-value decomposition (t-SVD).
This shortcoming motivates us to develop a new efficient randomized algorithm for the problem of tracking multilinear t-LRA. The main contributions of this paper are two-fold. A part of this paper has been presented at the 46th IEEE ICASSP 2021  [43]. Firstly, under CP decomposition, we propose a novel adaptive CP (ACP) algorithm for tracking online t-LRA of higher-order incomplete streaming tensors. ACP is fast and requires a low computational complexity and memory storage, thanks to the alternative minimization and randomized sketching. It can handle incomplete tensors derived from infinite data streams because it performs CP decomposition with constant time and space complexity that are independent of time index t. A convergence analysis is then provided to establish performance guarantees.