dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of 300 training samples, 300 tuning samples and 1500 test samples are generated with equal probabilities for each class. The real world datasets consist of seven real microarray gene expression datasets, and four face image datasets. The number of classes ranges from 3 to 10. All the datasets are preprocessed by normalizing each dimension of the data to zero mean and unit variance. The detailed information of these datasets is summarized in Table  1. SRBCT  (Khan et al. , 2001)  can be downloaded at http://research. nhgri. nih. gov/microarray/Supplement/. The high-dimensional dataset consists of multi-spectral imaging of three penicillium species: melanoconodium, polonicum and venetum. This data is studied in  Clemmensen et al. (2007). The ALL/AML dataset was studied in the seminal paper of  Golub et al.
(1999). It is available at http://www-genome. wi. mit. edu. The authors studied the data for binary classification between AML and ALL. However, it can be treated as a three-class dataset (B-cell, T-cell and AML). Leukemia microarray dataset is used in  Yeoh et al. (2002)  and available at http://datam. i2r. a-star. edu. sg/datasets/krbd/. MLL-leukemia dataset can be downloaded at http://research. dfci. harvard. edu/korsmeyer/Supp_pub/Supp_Armstrong_Main. html. The six remaining datasets are downloaded at the ASU feature selection repository http://featureselection. asu. edu/.
Experimental setups All algorithms are implemented in the R 3. 0. 2, and performed on a PC Intel i7 CPU3770, 3. 40 GHz of 8GB RAM. In our experiments, we use the cross-validation scheme to validate the performance of various classifiers. Each real dataset is split into a training set and a test set respectively containing 2/3 and 1/3 of the observations. The test set is used to measure the accuracy of various classifiers given by the training procedure. This process is repeated 10 times, each with a random choice of training set and test set. The reported computational results are the average results over 10 runs with different training sets. The stop tolerance of DCA is τ = 10 -5 while the stop tolerance of the coordinate descent method is = 10 -4. The starting point of DCA is zero. The The tuning parameters λ and L (number of used discriminant vectors) were chosen by using the tuning sets for synthetic data and via 5-fold cross-validation procedure on the training sets for real data, from the sets of candidate values in the range [λ min , λ max ] for λ and [1, C -1] for L.
Numerical results on real datasets The computational results (the average results on 10 runs with different training sets) given by 1,0 (DCA1), 1,0 (DCA2), 2,0 (DCA1), 2,0 (DCA2), OSGL, MSVMGL and ADCA are reported in Tables  4 5. We are interested in the efficiency (the sparsity and the accuracy of classifiers), the number of used discriminant vectors L, as well as the rapidity of these algorithms. The discriminant vectors can be used to visualize the datasets such as in Figure  1. Observing from Tables 4-5, we have the following comments.
Sparsity: The classifiers obtained by 1,0 (DCA1), 1,0 (DCA2), 2,0 (DCA1) and 2,0 (DCA2) are sparser than those obtained by OSGL, MSVMGL and ADCA on 9/11 datasets. ADCA and OSGL are slightly better than 1,0 (DCA1),   of the same type as OSGL. The training time of ADCA is quite good: ADCA is faster than 2,0 (DCA1), 2,0 (DCA2), OSGL and MSVMGL on most of the datasets.
Analysis of 4a3A library culture results. The mRNA purified from cells was reverse transcribed using SuperScript III or IV First-Strand cDNA Synthesis System (Invitrogen) as described for the STARR-seq assay  30  using a plasmid-specific primer (RT_Rev, Supplementary Table  S1 ), the cDNA was then amplified using primers Report_Fwd and Report_Rev (Supplementary Table  S1 ), and the products were sequenced on an Illumina HiSeq. 2500 in 2 × 125 bp high output mode. Cell plasmid DNA was amplified and sequenced in the same manner as the cDNA samples but using primers Plasmid_Fwd and Plasmid_Rev (Supplementary Table  S1 ). Selection of enhancer candidates. The Integrative Genomics Viewer (IGV)  43  was used to select candidate enhancers by visual examination in the proximity of six annotated genes of interest.
Analysis of enhancer allelic variants. The sequences of genetically polymorphic variants of a given enhancer, cloned from A. coluzzii colonies as described above, were analyzed for genetic relatedness. To generate neighbor joining (N-J) trees to depict the relationships between genetic variants for the same enhancer, complete sequences were aligned using MUSCLE within the package Molecular Evolutionary Genetics Analysis Mega version X  46  , and N-J trees constructed using Mega. When at least four variants were tested, bootstrapping was performed and bootstrap values are included on N-J trees. Scale bars of trees represent 0. 5, but each bar is a different length. The longer the 0. 5 scale bar, the more genetically similar the sequences. A workflow summary figure is shown in Supplementary Fig.
Acknowledgements We thank the  Center for Production and Infection of Anopheles platform (CEPIA) at the Institut Pasteur , and  Corinne Genève  and  Emma Brito-Fravallo ,  GGIV Institut Pasteur , for rearing mosquitoes. We thank  Alexander Stark ,  Research Institute of Molecular Pathology, Vienna  for plasmids and helpful advice. This work received financial support to MMR from  National Institutes of Health ,  NIAID  # AI121587 ; to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council, Support for Frontier Research, Advanced  Grant # 323173  AnoPath; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study sites and subjects We collected data on dyadic encounters (i. e. between a focal animal and another individual, table 1 for definitions of the key terms used in the study) in the four species. We limited our data collection to the philopatric sex, that is, males in chimpanzees and females in the other three species. We did so because it was not always possible to describe dyadic attributes, such as affiliative relationships, in the nonphilopatric sex due to frequent migrations. In total, we collected and analysed data on 813 approaches (table  2 ). YB collected data on the Kabasinguzi troop at the Kanyawara study site, Kibale National Park, Uganda, from May until December 2015. The troop was fully habituated to human presence  [23]  and all individuals were individually identified. During the study period, the group included between 39 and 44 individuals  [24].
Chimpanzees PF collected data on the Sonso community of Budongo Forest, Uganda, also from May to December 2015. The group was also fully habituated to human presence  [26]. At the time of the study, the community contained 75 individuals, with a core home range of around 15 km 2. Study subjects were adult (N ¼ 9: !16 years) and late adolescent (N ¼ 2: !13 -15 years;  [17] ) males.
Vervet monkeys SM collected data on two wild groups of vervet monkeys at the Mawana Game Reserve, Kwa Zulu-Natal, South Africa from July 2014 to March 2015. Both groups were well habituated to human observers. During the study period, group sizes varied from 45 to 56 individuals  [30]. Study subjects were adult (N ¼ 10: !5 years) females.
Data collection Each day, a randomly chosen focal individual was followed for the whole day by the respective observers. Encounters took place when the focal individual approached or was approached by another individual (hereafter: partner) at a distance of 5 m (olive baboons and vervet monkeys), or 10 m (chimpanzees), or 0 m (sooty mangabeys)  [6]. The identities of the partner and of other individuals (the audience) present within 10 m (olive baboons, sooty mangabeys and vervet monkeys) or 15 m (chimpanzees) of the focal were also recorded. We used different distance-based criteria for encounters and audience to better reflect species differences based on observations during pilot studies. We noted whether or not the focal animal or the partner produced a greeting call and the social role of the focal and the partner during an encounter (i.
Sooty mangabeys To calculate social bond strength between females, we used data from focal animal sampling  [31]. During focal follows we recorded grooming interactions continuously. In addition, we used instantaneous scan samples at 15 min to recorded proximity data, i. e. the identity of the nearest adult female within 5 m around the focal individual and the identities of all adult females within 5 m of the focal animal. Data on all occurrences of decided dyadic conflicts were recorded and subsequently used to calculate the female dominance hierarchy.
Data processing 2. 3. 1. Individual features For each encounter, we extracted the identity, dominance status and role of the focal animal (i. e. approaching versus being approached, table  1 ) and its partner. Dominance status was estimated with Elo-rating  [35, 36]. In brief, this method assigns ratings on an interval scale to individuals and these ratings typically correlate highly with ordinal ranks  [37]. The calculation process starts with each individual being assigned the same (arbitrary) rating. Subsequently, as each dominance interaction is evaluated progressively, ratings of individuals change: winners of dominance interactions/fights increase in ratings and losers decrease in ratings. The amount of change in ratings is determined by the expectation of the outcome of an interaction prior to that interaction: a highly expected outcome (a high-rated individual wins against a low-rated individual) will lead to small changes in both royalsocietypublishing.
It is important to stress, however, that the purpose of this study was not to explore directly the function of greeting calls in the four species but rather to examine the proximate mechanisms underpinning their production. It is also worth noting that our intention was to gather comparable data to obtain measures of dominance relationships and social bonds. Hence, we relied on established and partly species-specific observation and analysis methods that aimed at maximizing social -ecologic validity with regards to a given species. Whether these differences affect our conclusions remains unknown and future work should show whether such analytical variation influences study conclusions in a meaningful way (e. g. via simulations,  [37] ). Across all three species, we found no significant relationship between the presence of a high-ranking or well-affiliated individual and greeting call production.
Acknowledgement. We are grateful to the management and staff of the  Budongo Conservation Field Station , the  Tai Monkey Project in Cote d'Ivoire  and  IVP  project in South Africa, as well as  Jessica Rothman , for their support and assistance. We thank the  Uganda Wildlife Authority , the  Uganda National Council for Science and Technology , the  Ministere de la Recherche Scientifique  and the  Ministere de l'Agriculture et des Ressources Animales in Cote d'Ivoire  and the  Ezemvelo KZN Wildlife in South Africa  for permission to conduct the study. We thank the editor  Claudia Wascher  and two anonymous reviewers for helpful comments on the manuscript.
Acute pain situations Analyses of FG and questionnaire data were consistent and showed a core group of frequent acute pain situations in PwD (Figures  3  and 4 ). In particular, the sample of health professionals participating in the questionnaire survey identified acute five pain situations: 1) bathing and skin care (93. 1%), 2) dressing/change (82. 8%), 3) transfer/mobilization (82. 8%), 4) wound care and bedsores (55. 2%), and 5) blood tests (37. 9%). Eighteen FG excerpts relating to the subtheme "type" of painful situations in dementia were transcribed. The most cited situations were: transfer/mobilization (n=6), nursing care (n=5), hygiene care (n=5) and feeding (n=1). One participant reported no painful care situations in PwD. Participants' opinions in both groups coincided on the fact that a patient's mood and behavior are useful indicators of pain in this context, especially aggressive behaviors (eg, physical violence, insults), opposition, facial expressions (eg, sad face, grimace), vocalizations and body language (eg, tense, rigid, pulling or pushing away).
Data collection assessments Results from informal interviews confirmed that health professionals were used to assessing pain with the ALGOPLUS scale. Most filled out the items without difficulty and reported no additional burden. Cases of missing or unusable data were minor (35%), mostly found in the scales completed by professionals with little experience in geriatrics. The chi-squared analysis demonstrated a significant difference in the distribution of missing data between professionals with more than 5 years of experience in geriatric care and those with less than 5 years of experience (χ 2 =6. 28, p<0. 05). We estimated that the average time required to complete the scale was 2. 3 minutes.
The sample was extremely heterogeneous and included repositories developed by research funders, infrastructures, universities, and editors. All but three repositories do not apply a fee for uploading, storage or access to data. None of the repositories completely demonstrated all the items included in the indicator set, but three repositories (Dryad, Drum, EASY) metfully or partiallyall items. Flexibility of data-access modalities appears to be limited, being lacking in half of the repositories. Conclusions: Our evaluation, though often hampered by the lack of sufficient information, can help researchers to find a suitable repository for their datasets. Some repositories are more mature because of their support for clinical dataset preparation, contractual agreements, metadata and identifiers, different modalities of access, and long-term preservation of data.
The central role of data repositories in improving data sharing in clinical research was recognised in the context of a consensus exercise that developed principles and recommendation on sharing and reusing individual-participant data (IPD) from clinical trials  [5]. As of October 2018, the DataCite database, re3data, lists more than 2000 data repositories, hosting data and documents from different scientific disciplines, although only a tiny minority of them appear to host clinical study data  [8]. Several funders require that a data management plan that includes provision for data sharing is submitted along with the grant application and some have established dedicated data repositories  [9] [10] [11]. Increasingly, medical editors require that data underlying the results presented in journal articles are made available to other researchers and suggest repositories where data can be stored and identified  [12] [13] [14] [15].
Some universities and research institutions have established data-sharing policies and related data archives, mainly to improve dissemination and outreach activities. These institutional initiatives support any type of research data and clinical study data may, therefore, also be potentially stored and accessed through these systems. In 2014, Clinicalstudydatarequest. com (CSDR)  [16]  and Yoda  [17]  were launched. These platforms are not data repositories as suchinstead they facilitate the interaction between data generators (usually industry sponsors) and data users, allowing access to specific datasets made available by the study sponsors within a controlled analysis environment. Despite the advent of these repositories, initiatives, portal systems, and the gradual increase in the sharing of data from clinical studies, the role of data repositories appears, so far, to be relatively limited  [18, 19].
For instance, IPD meta-analyses are usually conducted by contacting the clinical investigators who ran and analysed the original studies, who usually retain the data themselves. It may be that many existing repositories are not adequate for clinical studies, as, for instance, they do not fully support the forms of restricted access often required for IPD datasets. On the other hand, clinical researchers and sponsors, especially in academia, may not be familiar with the resources available for long-term data management, and thus the storage and sharing options available to them. The information about the different repositories able to store clinical research 'data objects'the generic term used to refer to any document or dataset in digital formmay be useful in the development of the data-sharing plan.
Identification and selection of repositories We identified repositories, publicly accessible via the Internet, combining formal and informal iterative searching. The work was based on the activities of previous working groups within CORBEL and the IM-Proving Access to Clinical Trial data (IMPACT) Observatory  [5, [20] [21] [22]. We broadly searched the web using keywords such as 'open data and clinical trials' , 'clinical trial data sharing' , 'repository' , 'database' , 'clinical research' and 'study results' as well as the re3data database  [8]. Additional information was retrieved from the relevant literature, personal contacts, meetings, and conferences. This approach was not intended to be a systematic search, but it was considered the most appropriate and feasible in evaluating a field as dynamic and heterogenous as data sharing.
We included repositories with details available in the English language, so that we may have missed some repositories, e. g. some operating exclusively in Asia. We focussed on repositories developed by public or private not-for profit institutions (e. g. universities, research institutions) that enable storage, sharing, discoverability, and re-use of IPD from clinical studies and related study documents. We excluded clinical trial registries/databanks and patient registries, as they usually host health data originating in a non-research context, i. e. clinical practice, and trial registries as they contain protocol information and summary results (aggregate data). We excluded repositories developed by pharma companies as the focus was on repositories allowing the upload of data from non-commercial studies.
List of items assessed For each repository included in the analysis, we collected and assessed a pre-defined set of 34 items (Additional file 1). These features were proposed and agreed among the group of authors and built on the previous work and discussion within the CORBEL and the IMPACT Observatory projects. They were meant to provide a general characterisation of the repository, and included aspects used to help assess the repository's relative maturity and its suitability for clinical research data. We split the items into four groups: (1) General parameters, including scope, funding and establishment date, and description of the repository; (2) Data upload and storagein particular the practical steps required for depositing IPD and related documents, including potential costs; (3) Data management and accessboth quantitative and qualitative descriptions of the types of access provided and of the data, including an indication of the approximate number of clinical studies stored in the repository, as well as the type of material available; and ( 4) Discoverabilitythe details of metadata schemas applied and how those metadata are made available.
Data collection From April to June 2018, pairs of authors independently assessed eligibility for each candidate repository, collecting data and filling in a pre-defined form that spanned the 34 data items. The data extracted were then checked for consistency and discrepancies, which were resolved by discussion among the four authors involved in data extraction (RB, SC, CO, WK). The sources of information were the public pages of the repository websites, including usage contracts, usage guidelines, Q&A pages, FAQs, support pages, privacy policy pages, etc. , and those documents available after simple registration, as well as reports, publications, and other publicly available materials about the repositories, as press releases, and presentations. We did not contact the repositories directly to gather missing information or clarifications.
Data analysis The information collected was summarised and used to estimate the suitability of the repository for hosting clinical study data. Certification systems for data repositories exist, such as the CoreTrustSeal certification launched by the merging of World Data Systems and Data Seal of Approval  [23] [24] [25]. However, they provide basic requirements for repositories and we considered them to be not sufficiently specific for health research data. For the purpose of this study we selected the following eight features from the 34 data points and used them as indicators of suitability: (1) guidelines for data upload and storage, (2) support for data de-identification, (3) data quality controls, (4) contracts for upload and storage, (5) availability of metadata, (6) application of identifiers, (7) flexibility of access, and (8) repository long-term preservation.
com in April 2018 as part of the academic funder consortium and it is expected that this platform will enable data sharing from MRC-funded clinical trials. We also excluded repositories hosting health data collected in routine clinical practice (such as BioGrid Australia Limited) or population and survey data (such as the UK Data Service). Table  1  reports the main characteristics of the included repositories (and includes the URLs of their home pages). Half are generic repositories, meaning that they are not limited to a disease or clinical area, and in many cases are not even specific to clinical research, accepting data from a wide range of scientific disciplines. The number of IPD sets stored in the covered repositories varied from none to hundreds. A few repositories, such as those from Early Breast Cancer Trialists' Collaborative Group, the Immune Tolerance Network, and Vivli, reported the number of clinical studies on their website, but often estimating this number accurately was impossible due to a lack of clear search criteria or appropriate metadata.
Four repositories started their activity before 2000, eight between 2000 and 2010, and the remainder were launched after 2010. Most repositories do not apply a fee for uploading, storage or access to data. Notable exceptions are Dryad, Figshare, and Vivli that propose periodic or per-study fees for uploading and publication of data. Although the sample is highly heterogeneous, it is possible to delineate a group of repositories developed under the auspices of public research funders and infrastructures. For instance, in Europe, B2SHARE, funded by EUDAT, and Zenodo, funded by the European Organization for Nuclear Research (Conseil européen pour la recherche nucléaire: CERN) are part of a data-sharing infrastructure providing data services for researchers, scientific communities, institutions, and  Only some of them host IPD data from clinical studies, and, although they are not completely homogenous, they share some common features as they all refer to overall NIH policies for data preparation and dissemination, including anonymisation  [26].