dataesr/scientific-paragraphs-categorization huggingface dataset:
Our pilot case study, an investigation of leadership behaviors exhibited in emails from a FLOSS development project  (Misiolek, Crowston, & Seymour, 2012) , reveals that it is common for researchers to use a multidimensional coding scheme consisting of a significant number of codes in their research inquiry. Previous work has shown that not all dimensions in a multidimensional coding scheme could be applied fully automatically with acceptable level of accuracy  (Dönmez et al. , 2005)  but little is known if it is possible at all to train an optimal model for all codes using the same combination of features. Also, the distribution of codes is often times uneven with some rarely occurring codes having only few positive examples in the gold standard corpus. This paper presents our attempt in optimizing the initial ML model through feature exploration using gold standard data created from a multidimensional coding scheme, including codes that suffer from sparseness of positive examples.
Conclusion and Future Work We conclude that creating an optimal initial onesize-fits-all ML model for all codes in a multidimensional coding scheme using only a single feature combination is not possible when codes with sparse positive examples are present, and training data is limited, which may be common in real world content analysis projects in social science research. However, our findings also show that the potential of using a one-size-fits-all model increases when the size of positive examples for each code in the gold standard corpus are above 100. For social scientists who may not possess the technical skills needed for feature selection to optimize the initial ML model, this discovery confirms that we can create a "canned" model using a single combination of features that would work well in text classification for a wide range of codes with the condition that researchers must be able to provide sufficient positive examples above a certain threshold to train the initial model.
Acknowledgments This material is based upon work supported by the  National Science Foundation  under Grant No. IIS-1111107. Kevin Crowston  is supported by the  National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation. The authors wish to thank  Janet Marsden  for assisting with the feature testing experiments, and gratefully acknowledge helpful suggestions by the reviewers.
In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.
INTROCUCTION Recently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response. Among the indexation approaches of 3D models, we find the 2D/3D approach  (Haris et al. , 2014; Jain and Singh, 2013; Li and Johan, 2013; Petre et al. , 2010)  which is based on a set of images (the characteristic views, images of depths, the slices. ). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them.
Result and Performance In order to evaluate the measurement of retrieval performance, we examine the Recall-Precision graph for the shape descriptor proposed. "Precision" Equation15 measures that the ability of the system to retrieve only models that are relevant, while "recall" Equation16 measures the ability of the system to retrieve all models that are relevant. They are defined as : We compared the classification performance of the proposed method with two well known methods, that the descriptor of 3D Zernike moments  (Sit et al. , 2013)  and descriptor of moments invariants 3D  (Suk and Flusser, 2011). For each one of the chosen shape categories, we have calculated the average Recall-Precision graph by using all shapes of the test database by a query object Fig. 11. We can see that the proposed shape descriptors perform better than the 3D Zernike descriptor and surface moments invariants descriptor.
Contacted initiatives We developed and administered a structured survey tool for ILI practitioners and local leaders to characterize a sample of initiatives throughout Latin America (including Mexico, Central America, and South America) as well as the major Spanish-speaking Caribbean jurisdictions of Puerto Rico, Cuba, and the Dominican Republic. We began by searching broadly for potential initiatives by performing online keyword searches, including in project databases and websites of conservation and rural development organizations operating in the LAC region (for a list of search terms, see Supplementary Material). We identified additional initiatives through the networks of experts and organizations participating in the Landscapes for People, Food and Nature Initiative (LFPN, http://landscapes.
Data analysis We treated responses to the closed-ended questions as ordinal or binary variables, depending on the question. For instance, respondents reported on motivations according to their perceived level of importance (ordinal variable with four possible levels), while participation of each stakeholder group in the design and/or implementation of an initiative was reported as either present or absent (binary). We developed a set of indices to quantify the relative number of investments and outcomes in each domain, as well as the relative balance across all four domains. The "investment index" was calculated as the ratio of reported investments in each domain to the total number of possible investments (i. e. , the total number of pre-defined choices offered on the questionnaire) in that domain.
We normalized the ratio for each domain to a 25-point scale and summed these scores to derive an overall investment index, with possible scores ranging from 0 to 100. We calculated an "outcome index" in the same way. Although these indices do not necessarily reflect all core or associated investments and outcomes in a landscape, nor the magnitude of such investments and outcomes, they are useful for understanding the relative focus and breadth of each initiative across the four domains, as well as level of the "inter-sectorality" of the initiatives. We analyzed the raw survey data and the derived indices to assess the distribution of each variable as well as the associations among the variables and trends among the initiatives. We used analysis of variance and Pearson's product-moment correlation analysis performed on the indices and other continuous variables to understand the relationship between investments and outcomes in general, and to compare investments and outcomes across the four domains.
Acknowledgments We thank all of the survey respondents for taking time to share their experiences with integrated landscape management. Diana Vega  and  Camila Medeiros  provided excellent research assistance. We also thank  Alexandra Judd  of the  Iberoamerican Model Forest Network  for helping to contact with ILI practitioners from  Model Forests  within LAC. We gratefully acknowledge financial support from the  Gordon and Betty Moore Foundation  and the  CGIAR   Research Program on Water, Land and Ecosystems. We thank three anonymous reviewers for their valuable comments to improve the manuscript.
BBMRI, THE EUROPEAN BIOBANK INFRASTRUCTURE Most of our current knowledge of diseases, as well as available diagnostic assays and drugs, were obtained through systematic investigation of human biological samples and medical data. A key to such investigations is the facilitation of structured access to qualitycontrolled patient-donor samples and data in an ethically appropriate and privacy-compliant manner. For decades, human biological samples and data have been gathered in the so-called biobanks, that is, collections of biological samples with connected information about the donors and results of prior analyses. In the recent decade, several international initiatives have emerged to coordinate and harmonize biobanking, and to develop regionally and ultimately globally, standardized protocols and metrics.
Much important expertise and knowledge in applied sciences is by nature not codified but tacit and can only be transferred by direct contact between individuals concerned. Physical proximity supports knowledge and technology transfer as demonstrated by many successful biotech clusters around the world. The biobanking-related market includes companies in the fields of cryotechnology, reagents, plastic ware (eg, cryo tubes, vials and cell culture flasks), robotic sample processing systems, reagents and equipment for sample preanalytics, sample tracking, data management, biosafety and biosecurity, and, importantly, the many different analytical platforms and their attendant data analysis. The area of bioinformatics is especially important to biobanking, because bioinformatics-based tools are needed to link biospecimens with databases, to analyse data, to set up searchable catalogues and to exchange results.
Here, donors can compare their own provided data with the biobanks' aggregated data in real-time, in a protected environment, and furthermore they could be kept posted on results of studies involving their sample and data. With this in mind, BBMRI-ERIC is developing as one of its first activities, pan-European Common Services in the fields of Ethics and ICT, and it has in its statutes that research involving BBMRI-ERIC biobanksand by proxy research in BBMRI Expert Centresmust comply with the rules and procedures of the participating biobanks and European-wide accepted ELSI standards. It is thus of paramount importance that regulations and procedures designed to protect citizens and legally safeguard the trust of the public do not unintentionally hamper or prevent biomedical research that is arguably in the citizens interests.
Lifandis AS requires all industry projects to be research focused and approved by regional ethics committees, and the aim is to return all project data/results to the involved bio-and databanks. Currently, Lifandis AS has several ongoing industryfunded projects and a long list of potential new projects under discussion/negotiation. EGC (the Estonian Genome Center): EGC 31 is a university R&D institute managing the Estonian Biobank of 52 000 people and operating as an Expert Centre. EGC is running up-to date genotyping and sequencing facility (CSPro and ISO9001: 2008) providing bioinformatics and statistical support and has performed projects for many customers from EU and outside. The other entities are public domain activities, receiving public and private project funding. This work is licensed under a Creative Commons Attribution 3.
ACKNOWLEDGEMENTS We thank the people involved in the European BBMRI preparatory phase (PP), BBMRI-LPC and BBMRI-ERIC. BBMRI-PP was financially supported by the  European Commission  (grant agreement no. 212111 ). BBMRI-LPC is supported by the  European Community 's  7th Framework Programme  ( FP7/2007-2013 ) grant agreement no. 313010. BBMRI. seacknowledges the  Swedish Research Council  grant agreement no. 829-2009-6285. BBMRI. fiacknowledges the  Academy of Finland  (grant no. 273527 ). BBMRI-NL acknowledges the  Netherlands Research Council  grant  NWO 184. 021. 007. BIOBANQUES (BBMRI. fr)acknowledges the  National agency for research grant 'Investissements d'Avenir'. BBMRI-AT  acknowledges the grant from the  Austrian Federal Ministry of Science, Research and Economy  ( BMWF-GZ10. 470/0016-I/3/2013 ).
Spectral data was collected using a 10× microscope objective over the range 400-1800 cm -1 with a 10 sec integration time. The detector used was a 16-bit dynamic range Peltier cooled CCD detector  22, 23. SERS samples were prepared by mixing 25 l of GNP solution with 25 l of aqueous solutions of the probe molecule (R6G) at varying concentrations. In all cases, the nanoparticle number was estimated based on the concentrations of initial seed nanospheres. In the case of the aggregated nanospheres, nanoparticle numbers quoted for all experiments are those of the initial, unaggregated suspensions. Final nanoparticle concentrations (Nn) of 3 x 10 3 , 3 x 10 6 or 3 x 10 9 /mL were employed for the range of experiments. For the probe molecule, concentrations were varied over the ranges 0. 1 -10M (R6G), for each nanoparticle concentration.
The datasets are further separated when considering the Raman signal as a function of dye molecules per nanoparticle, as shown in Figure  6b. For each nanoparticle type, a similar SERS intensity is observed for substantially different regimes of the parameter I s / N n , depending on the concentration of nanoparticles. However, when considering the SERS intensity per nanoparticle, as a function of the number of R6G molecules per nanoparticle, a more continuous behaviour is observed for each nanoparticle type, over the concentration range studied, as shown in Figure  6c. In Figure  6c , a clear dependence of the SERS signal, and therefore enhancement factor, EF, on nanoparticle shape is observable. Although it is not accentuated in the log/log format of the graph, there remains a significant difference between the SERS per nanoparticle at a given concentration of dye per nanoparticle, in the sequence (aggregated) nanosphere < nanotriangle < nanostar.
Acknowledgements This work was supported by the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) , as well as the  ANR TheraGuIma  ( ANR-11-NANO-017 ). Thanks are also due to French program  ANR-12-RPIB-0010  Multimage, to Grenoble MRI facility IRMaGE and to beamtime at the  ESRF BioMedical beamline.
Purpose The aim of this study was to describe a number of electronic healthcare databases in Europe in terms of the population covered, the source of the data captured and the availability of data on key variables required for evaluating medicine use and medicine safety during pregnancy. Methods A sample of electronic healthcare databases that captured pregnancies and prescription data was selected on the basis of contacts within the EUROCAT network. For each participating database, a database inventory was completed. Results Eight databases were included, and the total population covered was 25 million. All databases recorded live births, seven captured stillbirths and five had full data available on spontaneous pregnancy losses and induced terminations. In six databases, data were usually available to determine the date of the woman's last menstrual period, whereas in the remainder, algorithms were needed to establish a best estimate for at least some pregnancies.
In seven databases, it was possible to use data recorded in the databases to identify pregnancies where the offspring had a congenital anomaly. Information on confounding variables was more commonly available in databases capturing data recorded by primary-care practitioners. All databases captured maternal co-prescribing and a measure of socioeconomic status. Conclusion This study suggests that within Europe, electronic healthcare databases may be valuable sources of data for evaluating medicine use and safety during pregnancy. The suitability of a particular database, however, will depend on the research question, the type of medicine to be evaluated, the prevalence of its use and any adverse outcomes of interest.
It is these limitations that have contributed to the expansion of the use of electronic healthcare databases, containing anonymised patient data, for evaluating the safety of medicine use during pregnancy. When evaluating the safety of medicine use during pregnancy, the reliability of exposure and outcome assessment are important considerations. Electronic healthcare databases can often provide detailed data on potential prescription drug exposure, although they lack information on non-compliance, over-the-counter medicine use (medicines purchased without a prescription) and the precise timing of exposure. In terms of outcome data, although information on pregnancy losses may be captured, the level of detail regarding congenital anomalies in the offspring can be limited, and this can hamper drug safety in pregnancy research using healthcare databases alone.
8 In Europe, the European network of population-based registries for the epidemiological surveillance of congenital anomalies (EUROCAT) comprises 43 registries of congenital anomaly registrations in 23 different countries. 9 These registries capture detailed and extensive outcome information regarding the specific types of congenital anomaly although there may be underreporting in some registries, and information on medicine exposure in utero can be limited. A study evaluating first-trimester exposure to the anticonvulsant lamotrigine and the risk of oral clefts in the offspring was the first study to use EUROCAT congenital anomaly data, including the medication exposure data routinely recorded by the registries, to carry out a risk assessment. 10 This work is now being developed further within EUROmediCAT, a Seventh Framework Programme study funded by the European Commission that aims to make more systematic use of electronic healthcare databases in combination with EUROCAT congenital anomaly data and build a European system for the evaluation of medicine use in pregnancy in relation to the risk of congenital anomalies.
METHODS A sample of electronic healthcare databases that captured pregnancies and prescription data was selected for this study on the basis of contacts within the EUROCAT network. For each participating database, an inventory was completed to provide information on the population covered, the source of the data captured and the availability of data on key variables required for evaluating medicine use during pregnancy as well as pregnancy outcomes. This inventory was completed on the basis of the data available within these databases between 2004 and 2012. Ethical and data access approvals were obtained from the relevant governance infrastructures.
RESULTS 1] [22]  Mid-way through the study, an eighth centre from France also offered to collaborate. 23 With the exception of the IADB. nl database in the Netherlands and the Clinical Practice Research Datalink (CPRD) in the UK, all databases involved the linkage of multiple individual databases to enable the capture of pregnancies, prescription data and additional information on potential confounding variables. For the remainder of this paper, linked databases will be referred to as a single database. A summary of the eight databases can be found in Table  1.
Population covered The total population covered by the eight databases was 25 million. The Danish and Norwegian databases covered the entire population in each country whereas those in the Netherlands, France and Italy covered a particular region (Northern Netherlands, Haute Garonne, Emilia-Romagna and Tuscany), and the databases in the UK captured a sample of the population (the CPRD capturing ~8% of the UK population and the Secure Anonymised Information Linkage (SAIL) database capturing approximately 40% of the population of Wales). The potential for loss to follow-up varied between databases with patients in Denmark, Norway and Italy only leaving as a result of death or emigrating from the country/region, whereas in other databases, exit from the database could result, for example, from the patient changing general practitioner (GP) or the GP practice withdrawing from the database.
Exposure data The databases in the UK captured exposure data based on the issue of a prescription whereas all other databases captured pharmacy dispensing data and therefore only  those prescriptions actually dispensed (Table  1 ). The majority of databases did not capture prescriptions issued to inpatients during a hospital stay; in the UK databases, however, some were captured if the GP entered them following receipt of a hospital letter. In Denmark, Norway and the Netherlands, all other prescriptions were captured, including private prescriptions, although a small number of rare or expensive medicines (e. g. cancer treatments) may not have been captured if they were dispensed at the hospital to outpatients. In the French database, all prescriptions for reimbursed drugs were captured, regardless of whether they had been prescribed by a GP or a specialist or had been issued privately.
In the Italian databases, prescriptions reimbursed by the National Health Service were captured; however, the majority of private prescriptions and prescriptions issued by a specialist to outpatients that were dispensed at a hospital pharmacy were not captured; the extent of hospital dispensing to outpatients varied by product and drug class. In the UK databases, the majority of prescriptions captured were those issued by a GP or non-GP prescriber within primary care. Private prescriptions and prescriptions initiated by a specialist in a hospital outpatient department were largely not captured, but any repeat prescriptions that were subsequently issued by the GP were captured. Determining the precise timing of exposure is crucial when evaluating medicine use and safety during pregnancy. To do this, accurate information on the first day of the last menstrual period (LMP) is required.
Pregnancies captured Table  2  summarises pregnancy-related information for each of the databases. The number of pregnancies captured per year varied from ~2000 in the Netherlands to ~88 000 in Denmark. This variation reflects the difference in the size of the source populations covered and the types of pregnancy outcomes captured. All databases captured live and stillborn deliveries with the exception of the IADB. nl database where stillbirth data were not available. Induced terminations of pregnancy and spontaneous pregnancy losses were captured in all databases, except those in the Netherlands and Emilia-Romagna (Tables  1  and 2 ). In the Norwegian Medical Birth Registry, however, induced terminations after 12 weeks' gestation were only registered when the foetus had a congenital anomaly and spontaneous abortions were underreported, particularly those occurring before 16 weeks' gestation.
Outcome data The most frequently studied adverse pregnancy outcome following in utero medication exposure is the risk of major congenital anomalies. It was possible to identify congenital anomalies in pregnancies that ended in a live or stillbirth in all databases, with the exception of the IADB. nl database. 1 In Denmark and Norway, congenital anomaly data could be obtained from the national birth registers, and in Italy, they were captured through the 'Certificate of Delivery Assistance'. In the CPRD, congenital anomalies could be identified from the infants' GP records; in Wales, they could be identified from linking to the congenital anomalies database; and in France, they could be identified from the Mother and Child Protection Centre database and the Antenatal Diagnostic Centre database for induced abortions.