dataesr/scientific-paragraphs-categorization huggingface dataset:
To generate training and validation datasets, we manually classified groups of pixels across six scenes with different cloud types as open water, opaque cloud, thin cloud with water underneath, thin cloud with iceberg underneath, or iceberg through clear sky. We extracted the top of atmosphere (TOA) reflectances and brightness temperatures for each classified pixel in the visible, NIR, SWIR and thermal wavelengths. We combined these spectral signatures and associated classifications to create training and validation datasets. Pixels from two scenes, one from each Landsat 7 and 8, were kept separate as a secondary validation dataset, while pixels from the remaining four scenes were randomly split into training and validation sets. We trained and implemented our multinomial logistic regression model using the Python machine learning scikit-learn library  (Pedregosa and others, 2011).
Close visual inspection of the iceberg pixel clusters revealed that in some scenes the edges of clouds missed by the cloud mask were falsely identified as icebergs. Rather than remove the scene from the dataset for abundant false positives (see Section 3. 3), pixel clusters that were identified as ice but that bordered cloud along >40% of the cluster's boundary were reclassified as clouds. The remaining clusters were classified as iceberg and outlined automatically by the computer, with each pixel or group of pixels stored as a unique iceberg polygon. To avoid inclusion of portions of icebergs within fjord mouths or along scene boundaries, the algorithm removed any iceberg polygon touching the ROI polygon or a scene edge. The analysis of iceberg parameters presented below uses the resulting shapefiles of iceberg polygons.
Iceberg size distributions Iceberg size distributions around Antarctica and Greenland have previously been characterized using both power law and lognormal distributions (e. g. Tournadre and others, 2012; Enderlin and others, 2016; Kirkham and others, 2017; Sulak and others, 2017). We tested power law, lognormal, exponential and Weibull distributions using the Python powerlaw  (Alstott and others, 2014)  library on two scenes' size distributions and found that a power law distribution of the form f(x) = x -α , where α is the fit parameter or slope of the probability distribution in log-log space (Fig. 5 ), weakly but consistently provided the best fit (see the Supplementary Material for an explanation and evaluation of fit parameters). Since our primary interest here is a consistent way to quantitatively describe the data and its variations through time, rather than necessarily capturing the exact shape of the distribution, we fit power law size distributions to each date's iceberg dataset using an x min value of 1800 m 2 (eight pixels).
Since iceberg decay is strongly controlled by iceberg geometry, we explored whether the change in Sermeq Kujalleq's calving could be inferred from a shift in iceberg size distributions. We applied the semi-automated algorithm, using the machine learning-based cloud mask, to the Landsat archive for Disko Bay from 2000-02 and 2013-15 (Fig. 6 ). We chose these year ranges to span the time period of greatest change in calving behavior of Sermeq Kujalleq  (Amundson and others, 2008; Joughin and others, 2008)  as well as avoid missing icebergs in whole or in part due to the stripping caused by the SLC failure on Landsat 7. Although methods exist to 'fill in' the missing data, these methods rely on landscape continuity through space or over time. The transient nature of icebergs means there is no reliable way to fill in the image gaps, thereby limiting the accuracy of an iceberg size dataset derived from this imagery.
Comparison of icebergs and parent glacier dynamics Recent numerical modeling of calving dynamics suggests that changes in calving style result from changes in glacier terminus relative buoyancy, defined as the water depth at the terminus relative to the water depth required for ice flotation  (Benn and others, 2017). We calculated the relative buoyancy of Sermeq Kujalleq's terminus to determine whether changes in relative buoyancy, and thus calving dynamics, could have driven the observed changes in iceberg sizes and ice cover in Disko Bay. We extracted ice surface elevation from pre-IceBridge (late May 2001/02 and 2013-15;no data were available for 2000)  (Thomas and Studinger, 2010)  and WorldView image-derived digital elevation models (DEMs;late June through late September 2013-15) (DEMs were created by the Polar Geospatial Center from DigitalGlobe, Inc.
ACKNOWLEDGMENTS This work was supported by  NASA Headquarters  under the  NASA Earth  and  Space Science Fellowship Program  (grant  NNX15AP08H , awarded to JS),  NASA  via the  Maine Space Grant Consortium  (award to JS), and the  US National Science Foundation Adaptation  to  Abrupt Climate Change IGERT Program  (grant  DGE-1144423 ). We thank  Jakob Abermann  and  Eva Mätzler  and others at  Asiaq, Greenland Survey , for assisting with project development and insightful discussions on iceberg detection and icebergs along the west coast of Greenland. We also thank the many marine operators and other residents of Nuuk and Ilulissat who provided information on iceberg hazards in Disko Bay and helped shape the goals of this project. Feedback from  Helen Fricker  and two anonymous reviewers significantly improved the manuscript.
Unix sh shell and Perl Apart from remote access through browsers to important web sites (e. g. the ncbi website: https://www. ncbi. nlm. nih. gov), we deliberately avoided to locally work with any menu-driven utilities based on clicking to analyze data. Indeed, in this case, the participants will never grasp what is really going on in making any analysis program work, particularly in terms of parameters set up. We chose to implement the course in a Unix(Linux) environment. Indeed, Unix shell systems provide access to many specialized utilities that can be executed by online commands  [18]. Above all, Unix pipe operator, ''|", makes it possible to create ad hoc pipelines by connecting the output of one command to the input of the next one. On the other hand, scripting languages, such as Perl and R, are appropriate and effective in solving many biological questions using significant amounts of sequence data or the outputs of diverse analyses, and accordingly several sessions covering both programming languages were included in the program.
Practical sessions involved the manipulation of all these different datasets. At the end of the first week the participants were able to write simple sh scripts implying the use of grep and sed, concerning notably the search for subsets of protein sequences and the use of simple commands (as for example identification and listing of sequence features, counting, substitution,. ) (see details in S4 Text). The second week was devoted to skills upgrading in Perl programming. Participants were introduced to Perl, detailing its abilities for substitution, selection, reading and writing files. Data types were among the most important concepts the participants had to grasp. The iterative way in dealing with data was part of the many simple scripts that were written as practical exercises (see details in S5 Text).
Methods and tools in sequence analysis The next two weeks were devoted to skills upgrading in sequence analysis, introducing key concepts in Biology, in Bioinformatics and in Genome science. More precisely, methods and tools were introduced for pair-wise sequence comparisons, multiple sequence alignments, construction of phylogenies, molecular evolution and motifs finding. These introductions were followed by intensive practical sessions using locally installed programs as well as web resources (see S2 Text and corresponding pdf documents for practical sessions). Examples of considered hands-on included, (i) gene and protein sequence composition respectively in base and amino-acids, (ii) practical procedure of the Smith-Waterman algorithm in aligning simple sequences, (iii) pairwise local and global sequence (nucleotide and protein) alignments (see S6 Text), (iv) running BLAST programs and interpreting their outputs (see S7 Text), (v) Multiple sequence alignments (ClustalW, ClustalOmega, MAFFT) (see S8 Text), (vi) Phylogenic tree construction methods, using essentially the PHYLIP package (see S9 Text), as well as (vii) aligning nucleotide sequences following their corresponding aligned proteins, particularly useful to ensure alignment at the codons level (see S10 Text).
Such alignment outputs were used as basis for exercises on calculation of synonymous and non-synonymous substitutions using PAML  [22]. All such practical examples were first run interactively, using command line instructions, and subsequently with automatic procedures relying on somewhat advanced Perl and shell scripts written by the participants. At this step participants were endowed with the required background in Bioinformatics methods and tools used in sequence analysis, in a Unix environment, to start large-scale genome analyses.
Large-scale genome analyses This topic represented the heart of the course program. Fundamental methods and tools used in Bioinformatics were introduced along with their effective application in real-case analyses concerning complete genomes and NGS or metagenomics data. Two weeks were devoted to large-scale analyses of completely sequenced Eukaryotic genomes. Lectures on genome resources, introducing notably major genome projects and associated published results, were followed by practical sessions in large-scale genome analyses, notably with studies of the main evolutionary processes related to intra-species duplication, conservation between species, horizontal gene transfer, inference of paralogs and orthologues and their classification  [23, 24]. Such practical sessions were based on real data (three yeast genomes), allowing participants to manipulate large amounts of data by writing Perl and sh scripts in a Linux environment.
The analyses concerned, notably, the characterization of genomes in terms of size, base compositions and amino-acid compositions of the corresponding proteomes (see details in S11 Text). In addition, the following topics were also considered: the comparison of genomes and determination of degree of intra-species duplications and of inter-species conservations, the determination of families of intra-species paralogs and of pair-wise Reciprocal Best Hits between protein sequences in distinct species and their clustering into families of orthologs using the mcl program  [25]  (see details in S12 Text and S13 Text). Results of intra-species and inter-species comparisons were further displayed using CIRCOS  [26]. Practical sessions were also devoted to the search for repeats, in the three yeast genomes, using the Tandem Repeat Finder (trf) program  [27]  (see details in S14 Text).
The specific aim of these sessions was the mastering of automation solutions, based on the re-use of previously written scripts for yeast genome analyses, to perform all pair-wise comparisons, Reciprocal Best Hits (rbh) determination and clustering, search for motifs in clusters of orthologs using the meme  [32]  program, as well as for genes subject to positive selection using the PAML  [22]  program (see S15 Text).
Moreover, in many biological fields of research using large-scale genomic datasets and information, there is a great variety of biological questions that need multidisciplinary competences and approaches to be addressed. In developing countries there is an urgent need to implement a strategy to encourage close collaborative efforts between computational scientists, mathematicians, statisticians and biologists to take part in the research and discovery initiatives in post-genome era. Additionally, young researchers should also re-think their understanding of Computer Biology: "Computational biologists are just biologists using a different tool" as stated in  [37]. It is possible to acquire programming skills that will make young researchers in developing countries, better able to implement, interpret and understand their own analyses, making by the way themselves at the same time better experimentalist as well  [37].
Many challenges lie ahead. Awareness is still lacking from mathematicians, statisticians and computer scientists in developing countries and particularly in Tunisia, about the huge opportunities offered by the publicly available data from genome sequencing projects to develop new ideas and take part in this extraordinary discovery adventure. It is their responsibility to make computational biology and genomics known as fields of research on their own. They are warmly invited to consider with attention the Singapore activities in these domains  [4]  as an example of what a small country can achieve. We hope this kind of achievement will encourage the Tunisian High Education and Scientific Research administrations to effectively act in favor of the development of these domains and the international community to contribute and help the set-up of such initiatives in developing countries.
Acknowledgments We acknowledge the help and support of  Prof. Fethi Sellaouti ,  President of the University Tunis El Manar  and  Prof. Hechmi Louzir , General Director of the  Institut Pasteur de Tunis, Tunisia , with whom the opportunity to set up this course existed. We thank our invited speakers for their participation and involvement in this course:  Gary Benson  ( Boston University. Boston, USA ),  Daniel Lundin  ( Stockholm University. Stockholm, Sweden ),  Guillaume Bourque  ( McGill University, Montreal, Canada ),  Roland Brosch ,  Philippe Glaser  and  Pedro Alzari  ( Institut Pasteur Paris, France ),  Jean-Michel Claverie  ( UniversiteAix-Marseille, France ),  Christos Ouzounis  ( Centre for Research and Technology, Hellas, Thessaloniki, Greece ),  Nabil Semmar  ( Institut Superieur des Sciences Biologiques Appliquees de Tunis.
e. , by comparing different categorization and functional form relationships). The aim of this paper is to analyze the implications of using different category-construction frameworks and aggregation levels on the classification systems and its impacts on the quality of the resulting FTG model. More specifically, it aims to address the following research question: does the aggregation level of establishment categories have an influence on FTG modeling patterns and accuracy? To address that question, three assessments and the resulting comparative analyses are proposed based on a comprehensive dataset of more than 2900 establishments: the first assessment addresses the comparison of constant FTG estimations for three different aggregation levels of activity-based categories; the second derives from the first, such that each activity-based category classification is completed through workforce classes (to include a first vision of the impact of employment on FTG) but continues to deploy constant estimations; the third aims to assess the impact of searching the most suitable functional form for each category (extending the works of Holguin-Veras et al.
First, the constant and a variable term are assessed statistically, i. e. , for each functional form all three following possibilities are assessed via linear regression: (i) a constant, (ii) a function of employment without constant term and (iii) a function with both constant and employment-based coefficients. The parameters for the models are estimated using sandwich estimators that are robust to outliers  (Freedman, 2006). These robust estimators depend on asymptotic properties, therefore the resulting estimators would not always fit the t-distribution which is a necessary assumption for the t-test  (Imbens and Kolesar, 2016). As this can induce bias in estimations made with small datasets, the authors applied a degree-of-freedom correction on standard errors (Imbens and Kolesar, 2016).
Data, category description and statistical relevance for average and functional form modeling To conduct the proposed analyses, this paper uses data issued from the French Urban Goods Surveys of 1995-1998. For a detailed description of the surveys, readers can refer to  Ambrosini et al. (2010). This dataset was selected because of the sample size and the variety of sectors covered, which is rare in FTG studies. The surveys used for the data collection comprise three parts: the first one describes the establishments' attributes, the second studies the pickup and delivery operations, and the third presents the truck driver's patterns, vehicle rounds and paths. Gonzalez-Feliu and Peris-Pla, 2017) and constitute, to the best of our knowledge, the most extensive dataset available 2. For FTG, the authors use a set of data extracted from the first two parts of the survey.
The data were collected from three cities with different characteristics (Bordeaux, Dijon and Marseille) and include 2,970 establishments and 11,588 freight trips, including both attraction and production. Although this dataset is outdated, it provides a unique opportunity to assess the modeling implications of different aggregations of classification systems because of the number of observations within each sector: 2,970 establishments with at least one operation of any type, 2,613 establishments with at least one reception, and 1,500 establishments with at least one expedition. Moreover, the FTG rates per establishment within each category were considered homogeneous independent of the city when designing the survey; thus, the three sets can be used jointly for modeling purposes  (Ambrosini et al.
, 2010). This study focuses on FTG, adding both attraction and production. FTG is the focus for two reasons: first, FTG, without specifying the nature of the operation, can be useful for some planning purposes (identification of truck types and mileages for congestion estimation, definition and dimensioning of parking and delivery bay facilities, etc. ), and making no distinction between the type of operations provides a larger dataset to enhance the statistical significance of the results. The classification system used to group the data within categories is an adaptation of the classification used in the French urban goods surveys (and resulting in both the data collection and descriptive analyses). This classification results from an aggregation of NAF codes (the French declination of NACE codes).
It is noteworthy that data collection designs should be in line with the level of aggregation that will then be selected for modeling. As the aggregation level is the central theme of this paper, the number of observations in each category is evaluated to check whether they meet the necessary threshold to be considered relevant for defining FTG patterns and those for each category. This threshold is important when addressing the mean estimation and regression-based analysis, as the normal distribution hypothesis is often made when conducting statistical tests (such as Student's and Fisher's tests) and ordinary-least-squares regressions. This hypothesis can be assumed for a number of observations equal to or higher than 30, thus obeying the Law of Big Numbers  (Wonnacott and Wonnacott, 2001).
Those categories are defined from the initial categories defined in the French surveys  (Ambrosini et al. , 2010) , grouping then those that did not meet the defined requirements (i. e. , having less than 6 observations) into the closest category in order to have the minimum number of observations for modeling purposes. A synthesis of the three activity-based levels of disaggregation is presented in Table  1. Table  2  shows the percentage of categories meeting the threshold of observations to be considered for inferential statistical analyses for each aggregation level. In the current context, it is difficult to split the dataset in two representative subsets to provide a construction-prediction test for all models (several categories, mainly for activity-workforce classifications, present less than 30 individuals, which makes difficult to split the complete dataset into two representative subsets).
Moreover, the results show that functional forms that relate FTG to employment can be found, and their quality remains similar for all aggregation levels, so the choice of disaggregating data is related to the use of the model and the resources used to develop it. A second implication is related to the use of the proposed results by practitioners, mainly related to the data inputs available to them for application. As shown above, functional forms depend on different explanatory variables. In this paper, only employment was used, since for French surveys, data on commercial areas or income are not publicly available. Data on employment were collected in the surveys, and thus, it was possible to identify the proposed functional forms. However, employment is often publicly available but only as a range (in France, in the USA, and in Sweden, among other countries), which in several cases refers to the entire company and not to the single location considered for FTG analysis.
The results in this paper reveal that depending on availability, there are two ways to overcome this challenge: (i) converting range data into an average employment rate for each establishment; or (ii) using paid datasets that disclose estimated or real numbers of employees. For (i), the accuracy of the FTG models will be reduced, not because of the models' accuracy but of the data input bias related to the need of estimating average employment rates, while for (ii), the cost will be increase because of acquiring the application data but the estimations would be more accurate. Finally, it is important to address operational and data collection implications. As shown in the analysis, for some complex categories for which it is difficult to collect data, the influence of data aggregation on the model quality is low, thus there is no need for much disaggregated data to produce FTG estimates.
This is interesting in categories 2-craftsmen), 4-wholesalers) and 7-tertiary), where it is very difficult to have a representative sample of all subcategories, but it is possible to have a good sample for the overall category. For some sectors, such as, sector 5-department stores), the main problem is finding enough establishments in a city of a single sub-category. In those cases it is valuable to know that an aggregated categorization can lead to models with similar accuracy than a disaggregated one. Therefore, a less detailed data collection can however lead to a good assessment model if the most suitable functional form is identified and assessed. Moreover, since the availability of databases and the budget to conduct extensive surveys vary across cities (Holguin-Veras and Jaller, 2014); the results presented in this paper can help practitioners to design samples requiring less resources for data collection in certain sectors while ensuring robust and statistically significant samples.
The limitations of the study are mainly related to the incorporation of additional variables that capture logistics decisions that differ by type of activities and to the transferability of the results. Results have also their limits due to data availability that did not allow the application of a prediction dataset different from the construction dataset. For the former, the introduction of nonlinear functional relationships is an important step in adapting the model to the effects of shipment size and mode changes on FTG, but further research is necessary in identifying relevant and practical variables that can explain FTG generation patterns, mainly in urban areas (where area, frontline and other variables are starting to be used but its use remains less generalized than employment). In terms of transferability of the findings, a direction for further research is to compare data from different countries, e.