dataesr/scientific-paragraphs-categorization huggingface dataset:
Simple ontology alignments, largely studied, link one entity of a source ontology to one entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. Although different complex matching approaches have emerged in the literature, there is a lack of complex reference alignments on which these approaches can be systematically evaluated. This paper proposes two sets of complex alignments between 10 pairs of ontologies from the well-known OAEI conference simple alignment dataset. The methodology for creating the alignment sets is described and takes into account the use of the alignments for two tasks: ontology merging and query rewriting. The ontology merging alignment set contains 313 correspondences and the query rewriting one 431.
Those in  [20, 21, 35]  rely on statistical methods and correspondence patterns and the one in  [18]  deals with genetic programming. Finally, the approach in  [22]  uses path-finding algorithms combined with statistical techniques. Despite the progress in the field, there is a lack of reference alignment sets on which the complex approaches can be systematically evaluated. Most efforts on evaluation are still dedicated to the matching approaches dealing with simple alignments. Systematic evaluation of them has been carried out over the last fifteen years in the context of the Ontology Alignment Evaluation Campaigns (OAEI)  1. Even though this well-known campaign proposes a task-oriented benchmark (the OA4QA track  [28] ), it does not propose a complex alignment benchmark. This paper proposes two alignment sets to extend the OAEI conference track dataset  [3, 36]  with complex alignments for two task purposes: ontology merging and query rewriting.
While matching evaluation has been focused on simple alignments, complex evaluation has been addressed to a lesser extent. Although a large spectrum of matching cases are proposed in the OAEI, e. g. , involving synthetically generated or real case datasets with large or domain-specific ontologies, these datasets are limited to alignments with simple correspondences. A notable alignment set has been proposed between biology ontologies in  [19]. In opposite to "classical" alignments, the correspondences of these alignments, called "compound alignments" involve entities from more than two ontologies. For example, o 1 :A ≡ o 2 :B o 3 :C is a compound correspondence. We consider these correspondences as complex since one of its member contains a constructor but it is out of the scope of our study.
In order to evaluate the recall of his approach  [35]  proposed an algorithm to create an evaluation data set. This data set is composed of a synthetic ontology containing 50 classes with known Class-by-attribute-value (a correspondence pattern) correspondences with DBpedia and 50 classes with no known correspondences with DBpedia. Both ontologies are populated with the same instances. The approach of  [21]  estimated their recall based on the recurring pattern between DBpedia and Geonames: ∃dbpedia:country. {theCountryInstance} ≡ ∃geonames:countryCode. {theCountryCode} where theCountryInstance is a country instance of DBpedia such as dbpedia:Spain and theCountryCode is a country code such as "ES". They estimated the number of occurrences of this pattern between these ontologies and calculated the recall based on this estimation.
The approach of  [22]  manually created a set of reference correspondences between two ontologies on which their approach was evaluated. On the nine reference correspondences, only two can not be expressed with simple correspondences. The closer approach to ours is from  [13]  who extended the conference dataset with complex alignments to evaluate their knowledge-rule based approach. However, the methodology used for the construction of the dataset is not specified and the dataset is not available online. In order to evaluate and compare alignment approaches, we need evaluation alignment sets. As discussed above, complex matchers have been evaluated on custom evaluation alignment sets. When the evaluation used a reference alignment, manually or automatically created, it usually contained correspondences which match only one pattern (Class by attribute-value for  [35] , attribute-value = attribute-value for  [21] ).
Approaches using complex correspondences for a given purpose (query rewriting for example), also propose alignment sets created for their needs, even though they have not been used for matcher evaluation. For instance, the authors of  [16]  present a set of complex correspondences used for query rewriting  3. However, they are not in a reusable format and only concern a pair of ontologies. In  [30] , complex correspondences between agronomic ontologies were manually created for query rewriting on the LOD. Regarding task-oriented evaluation, the authors of  [12]  propose ontology alignment evaluation methods for different task purposes: thesaurus merging, and data translation. The OA4QA track 4  of the OAEI  [28]  evaluate the suitability of an alignment for the task of query answering. This evaluation dataset consists in the conference track ontologies populated with DBLP, 18 queries over these ontologies and simple reference alignments.
Complex alignment set The alignment sets are available at https://doi. org/10. 6084/m9. figshare. 4986368. v7 under CC-BY license. They are expressed in first-order logic (FOL), EDOAL  [5] , and OWL for the ontology merging alignment set. EDOAL can express transformations as well as logical relations between entities. We have considered this language instead of others (SWRL, etc. ) as it is integrated in the Alignment API and extends the Alignment Format (standard de facto in OAEI campaigns). Before describing the proposed alignment sets, we introduce the Conference dataset from which our dataset have been built.
The conference dataset The conference dataset 5  was proposed in  [37]. It has been widely used  [36] , especially in the OAEI where it is a reference evaluation track. This dataset is composed of 16 ontologies on the conference organisation domain and simple reference alignments between 7 of these ontologies. These ontologies were developed individually. The motivation for the extension of this dataset is that the ontologies are real ontologies (as opposed to synthetic ones), they are expressive and largely used for evaluation in the field. Moreover, the reference alignments of simple correspondences between these ontologies are available. We chose five ontologies among the ones in the reference simple alignment for their different number of classes (c. f. Table  1 ): cmt, conference (Sofsem), confOf (confTool), edas and ekaw.
Evaluation of complex matchers As populating the ontologies to match with common instances would add new considerations for the evaluation dataset (e. g. , choice and quantity of the in- stances), we chose to evaluate the available approaches that do not need common instances, as those described in  [23, 24, 13]. We evaluated the matchers on the correspondences they output. Here we only evaluate the complex correspondences output as simple correspondences are taken as input by the approaches. For ontology merging, we only consider the SROIQ compliant correspondences output by the approaches. The correspondences output by the matchers were classified into true positive or false positive by comparison with each reference alignment set. A true positive is semantically equivalent to a correspondence in the reference alignment.
Next, families were selected for inbreeding to fix the alleles. The approach is described in more detail by  Peirce et al. (2004). The result is a large number of inbred strains in which the alleles from B6 and D2 were recombined and redistributed. The BXD strains are segregating over five million variants that distinguish the parental B6 and D2 strains and all of the strains have been densely genotyped. Over 10 brain regions have been subjected to microarray analysis of gene expression and there is a freely available database 1 consisting of more than 5,000 phenotypes contributed by many laboratories. This includes over 15 alcohol-related data sets in which the BXD family has been used to measure alcohol acceptance, consumption and preference  (Phillips et al. , 1994; Rodriguez et al. , 1994; Gill et al.
Drinking in the Dark Alcohol consumption was evaluated using the DID method  (Rhodes et al. , 2005). The protocol calls for 4 consecutive days of testing. Each day, starting on a Tuesday 3 h after lights were turned off, the water bottles were removed from the cages and replaced with 15 ml centrifuge tubes filled with 20% (v/v) ethanol from 95% USP ethanol. On days 1-3 (Tuesday through Thursday) the length of exposure was 2 h and on the 4th day (Friday) the exposure was 4 h. No alcohol was offered in the intervening period (Saturday through Monday). This protocol was repeated weekly for 5 weeks. Tubes were weighed immediately before and after the exposure period. Volume consumed was converted to g/kg body weight of ethanol. Subsequent weekly 2 or 4 h measurements were averaged by strain and used as the dependent variable for data analysis and QTL mapping.
Data Analysis Statistical evaluation of daily alcohol intake was performed by ANOVA testing in R using the lm function for a 1 betweensubjects variable (strain). Heritability at each time point was estimated from the ANOVA results by ss strain /ss total (where ss = sum of squares,  Belknap, 1998). Average weekly 2 h or 4 h intake was used to compute slopes and intercepts for each strain over the 5-week observation period. Genetic correlational analyses among the DID phenotypes, between the DID phenotypes and BXD legacy phenotype data, and traditional quantitative trait loci (QTL) analysis were performed using a combination of R and GN software  (Sloan et al. , 2016)  1. Traditional interval mapping in GN was performed using a simple regression method (Haley-Knott or HK) to compute QTL probability given strain genotypes and alcohol intake averaged by strain  (Chesler et al.
GEMMA software is a computationally efficient LMM method for QTL mapping while explicitly accounting for genetic non-independence within each sample  (Zhou and Stephens, 2012). Even for small sample sizes, after running Haley-Knott QTL mapping, GEMMA potentially allows for explorative fine-tuning of results at the SNP level. At each time point, and for each of 7,320 SNPs and phenotypes, we fitted the LMM using a kinship matrix K computed over the SNP genotypes. We also estimated significance thresholds with GEMMA using a permutation approach computing results 1,000 times while shuffling the phenotypes but keeping the genotypes and K the same  (Churchill and Doerge, 1994). From every permutation we stored the highest Wald-test p-value in an ordered set and set the significance threshold at the 95th percentile and the suggestive threshold at the 67th percentile.
For our data set, the average GEMMA significance threshold was LOD 4. 1 and the average suggestive threshold was at LOD 3. 3. GEMMA was included as a mapping method for two main reasons. The first reason is that additional power can be gained when using mixed-model association methods. The increase in power results both from accounting for phenotypic covariance due to genetic similarity and by conditioning on associated markers as opposed to a single candidate locus  (Yang et al. , 2014). The second reason is that the impact of background structure on QTL detection has not been rigorously evaluated in BXD data sets. Thus, GEMMA can be applied in addition to traditional HK mapping to minimize detection of false positive QTLs resulting from kinship. QTLs identified by both approaches are unlikely to result from genetic similarity.
Using both approaches has advantages over using a single mapping model because genetic relatedness is not addressed by the HK model and our data set is small (e. g. , underpowered) for GEMMA. For both HK and GEMMA QTL mapping methods, a 1. 5 LOD drop from the top marker was used to define QTL confidence interval regions. Loci detected using traditional HK mapping (suggestive threshold or above) that were also detected using the secondary GEMMA method (LOD > 3) were considered for further analysis. Enrichment analysis was performed using tools available at Enrichr  (Kuleshov et al. , 2016). Default settings were used for Enrichr. B6 and D2 polymorphic genes with SNPs and/or small insertions/deletions (InDels) were identified using tools available at the Sanger Institute Mouse Genomes Project 2   (Keane et al.
, 2011). The Sanger website provides annotations for variants and, for our analysis, a SNP or InDel was considered to be of potentially high impact if it was annotated as a "coding sequence variant, " "feature elongation, " "feature truncation, " "incomplete terminal codon variant, " "initiator codon variant, " "mature miRNA variant, " "missense variant, " "NMD transcript variant, " "regulatory region ablation, " "regulatory region amplification, " "regulatory region variant, " "splice acceptor variant, " "splice donor variant, " "splice region variant, " "stop gained, " "stop lost, " "TF binding site variant, " "TFBS ablation, " "TFBS amplification, " "transcript ablation, " or "transcript amplification. " A large BXD database of hippocampal gene expression profiles generated from 67 naïve BXD strains [Hippocampus Consortium M430v2 (Jun 06); GN110;  (Overall et al.
Trait Covariation With Initial Alcohol Intake Trait data have been collected for the BXDs since the generation of the first BXD cohort in the late 1970's and much of this data is available in the BXD Published Phenotypes database available on GN. We queried this database to retrieve traits that were significantly correlated with W1. 4 h, a heritable and QTL modulated trait associated with alcohol intake at early exposure. Forty-one traits were significantly correlated (p < 0. 005) with the    S6 ).
Traditional QTL mapping incorporates the mean of the dependent measure for each BXD strain. Power to detect QTLs is derived largely from the number of strains  (Belknap, 1998; Andreux et al. , 2012). However, the traditional mapping method used for BXD data does not account for population structure and may result in false positives. To account for this, we mapped QTLs using both a simple regression method (HK) in GN that does not account for kinship, and another mapping algorithm (GEMMA) available in GN, version 2, that does. Even though GEMMA is not typically used for smaller data sets, several QTLs were replicated using both methods, albeit at a suggestive level. Both methods used the same marker panel and dependent variables for alcohol intake. QTLs detected by both methods are expected to be more robust against identification of false positives due to genetic relatedness compared to using either model alone (see methods).
With 39 strains and a variable number of replicates within strain, our study was only powered to detect QTLs of large effect. As expected, we did not identify any significant loci. However, using both mapping methods we identified: suggestive QTLs associated with variation in alcohol intake during early exposure (W1. 4 h) on Chr 2, 6, and 12, and suggestive QTLs associated with variation in sustained intake during later weeks on Chrs 4 and 8. We were also able to identify a QTL on Chr 15 associated with progressive alcohol intake. To assess whether these QTLs are likely to reach significance if more strains are added we winsorized (transformation of the data by limiting extreme values) the existing 39 strain data to better fit assumptions of normality assumed when using HK and GEMMA mapping methods.
One of the great advantages of a systems approach in the BXD panel is the ability to find associations between a trait of interest and other phenotypes generated in-house, and by others, in the extensive BXD trait database available at GN. Not only were we able to nominate candidate genes for suggestive QTL related to initial, sustained, and progressive alcohol intake, but we were also able to leverage 1000s of legacy trait records available for the BXD strains to associate variation in initial alcohol consumption with variation in metabolic, metagenomic, immune, and behavioral traits in the BXDs (Supplementary Table  S4 ). Some of these associations were expected (e. g. , correlations with depressionand anxiety-related traits and alcohol intake; and covariation with morphine and other alcohol response traits), while others were novel and may uncover new areas of interest.
Background: Reliable information reporting systems ensure that all malaria cases are tested, treated and tracked to avoid further transmission. Botswana aimed to eliminate malaria by 2018, and surveillance is key. This study focused on assessing the uptake of the new malaria casebased surveillance (CBS) system introduced in 2012, which captures information on malaria cases reported in the Integrated Disease Surveillance and Response (IDSR) system. Methods: This was a retrospective descriptive study based on routine data focusing on Ngami, Chobe and Okavango, three high-risk districts in Botswana. Aggregated data variables were extracted from the IDSR and compared with data from the CBS. Results: The IDSR reported 456 malaria cases in 2013 and 1346 in 2014, of which respectively only 305 and 884 were reported by the CBS.
Botswana adopted the IDSR system according to the World Health Organization (WHO) Regional Committee for Africa 1998 recommendation, with the goal of tracking all epidemic-prone diseases and important public health conditions. 8  The IDSR has been the mainstay for passive malaria surveillance, with weekly reporting on basic indicators, and it is the primary source of information for locating malaria cases and prompting action nationwide. As part of the requirements for surveillance in malaria elimination countries, the CBS system was introduced in 2012 in all districts to report on the malaria elimination indicators. CBS captures data on every positive malaria case, which leads to an immediate investigation at household level and detection of other possible associated cases. While IDSR and CBS are uniquely different reporting systems for malaria deployed in parallel, they complement one another: IDSR contains information on the total number of malaria cases diagnosed (confirmed and unconfirmed) and deaths, and serves as an early warning system, while CBS reports details on confirmed malaria cases only, looking specifically at demographic characteristics, epidemiological and entomological data and other information helpful for the detection of possible cases that might have arisen from the index case.
Public Health Action which is taken as the gold standard. No studies have previously compared these two systems for malaria elimination programmes in Africa. It is therefore imperative to evaluate the CBS system against the IDSR in Botswana, to identify gaps that need to be addressed. Without further improvement and strengthening of the reporting systems, malaria elimination will likely be unachievable, as late reporting and investigation will lead to missing cases, resulting in onward transmission. The aim of the present study was to evaluate the uptake of the new malaria CBS system in comparison to the IDSR system for the completeness and quality of malaria data reported between 2013 and 2014 in Botswana. Specific objectives were 1) to compare the number of malaria cases reported through the IDSR and CBS systems for nationwide data (2013-2014), 2) to assess the completeness and quality of data reported via the CBS system in the three districts with the heaviest malaria burden (2013-2014), and 3) to determine the characteristics of cases with missing data reported via the CBS system in these three districts (2013-2014).
When an RDT result is positive, the protocol should be recorded in the IDSR and the patient should be notified immediately through the CBS system. The reporting tools for both CBS and IDSR are also available at all health facilities. Health officers at health facilities and in the communities collect data through both passive and active surveillance through the IDSR and CBS systems. In consultation rooms, there is a tally sheet that feeds both the IDSR reporting tool and the CBS reporting tools. Once a patient is clinically diagnosed or confirmed through RDT as having malaria, the case is recorded in a tally sheet, which is then used to compile the weekly IDSR report. The case is then reported immediately via the CBS forms. Active surveillance is conducted by a team of health workers, including facility nurses, who travel with the CBS data collection tools, through which they immediately notify cases while they are working in the field.
The same clinicians in the health facilities then collate the collected data. Subjects identified by the CBS system are entered in the IDSR, as they are tallied in both systems. The data source is the facility-based tally sheets, which capture both unconfirmed and confirmed malaria cases. As the figures reflect confirmed malaria cases, the figures from the IDSR and CBS should tally. The IDSR reports confirmed and unconfirmed cases separately, and the definition of confirmed cases is the same in the IDSR and CBS. In the analysis, only data on confirmed cases were extracted from the IDSR so as to be able to make a proper comparison of information collected in the two systems. It is expected that the same number of cases would be obtained from both systems. 'Correct data' refers to data required by the CBS system, and 'missing data' refers to empty fields where data were not filled in.
Integrated Disease Surveillance and Response The IDSR serves as an early warning system for all epidemic-prone diseases in the Ministry of Health (MoH). Data on aggregated unconfirmed and confirmed malaria cases and deaths from all 29 districts are entered into the IDSR on a weekly basis. At facility level, all patients, i. e. , unconfirmed cases, confirmed cases and deaths, are tallied in the OPD register. Thus, once a case is confirmed as a true (RDT or microscopically diagnosed) or clinically diagnosed malaria case, this is recorded. A weekly summary of the OPD register at each health facility is completed using the IDSR tool, then the DHMT summarises the health facility reports.
Data validation All patients diagnosed with malaria from 1 January 2013 to 31 December 2014 were included in the study. Data variables were extracted from the IDSR, which is accessible at district level. CBS data variables for each patient included basic sociodemographic information, date of diagnosis, date of reporting, date of case investigation and case classification, including indigenous, imported and unclassified cases. The variables from each surveillance system were then compared to measure the level of agreement.
Analysis and statistics The selected data were imported into an EpiData database and analysed (v. 3. 1 for entry and v. 2. 2. 2. 182 for analysis, EpiData Association, Odense, Denmark). Summary descriptive statistics were completed, including the χ 2 test for categorical variables and the t-test for continuous data. A two-tailed P  0. 05 was used for statistical significance.
RESULTS In 2013, 456 malaria cases were reported in the IDSR and 305 in the CBS (Figure). There was a significant increase in the number of cases reported in each system in 2014: 1346 in the IDSR and 884 in the CBS. The proportional difference between the reporting systems was not significantly different (33% for 2013 vs. 34% for 2014, P = 0. 8). There were substantial differences in correctly reported data in the CBS for the three high-risk malaria districts, Ngami, Chobe and Okavango (Table  1 ). The greatest number of cases was reported by Okavango district (72%). The key malaria reporting indicators with the greatest variability in the districts of Ngami, Chobe and Okavango in proportion to correctly reported indicators were case identification number (respectively 28%, 69% and 62%, P  0.