dataesr/scientific-paragraphs-categorization huggingface dataset:
Islam dataset The count table for the Islam et al. [16]  dataset was downloaded from the Gene Expression Omnibus with accession number GSE29087  [35]. The Islam dataset represents 44 embryonic fibroblasts and 48 embryonic stem cells in the mouse, sequenced on an Illumina Genome Analyzer II. Negative control wells were removed and only the 11,796 genes with at least five positive counts were retained for analysis. For the simulation, we generated datasets with two groups of 40 cells each.
Trapnell dataset The dataset from Trapnell et al. [36]  was downloaded from the preprocessed single-cell data repository CON- QUER (http://imlspenticton. uzh. ch:3838/conquer). Cells were sequenced on either an Illumina HiSeq 2000 or HiSeq 2500. We used only the subset of cells corresponding to the 48 h and 72 h timepoints of differentiating human myoblasts to generate two-group comparisons. Wells that did not contain one cell or that contained debris were removed. We used a more stringent genefiltering criterion than for the Islam dataset and retained the 24,576 genes with at least ten positive counts. The simulated datasets contain two conditions with 75 cells in each condition, thereby replicating the sample sizes of the Trapnell dataset.
Software implementation An R software package for our novel scRNA-seq simulation framework is available from the GitHub repository for this manuscript (https://github. com/ statOmics/zinbwaveZinger). Additionally, all analyses and figures reported in the manuscript can be reproduced using code in this GitHub repository. The ZINB-WaVE weight computation is implemented in the computeObservationalWeights function of the Bioconductor R package ZINBWAVE. ZINB-WaVE-weighted EDGER can be implemented using the glmWeightedF function from the ZINBWAVE package, while ZINB-WaVE-weighted DESEQ2 can be implemented using the native nbinomWaldTest function from the DESEQ2 package. More details of the ZINB-WaVE-weighted analysis can be found in the ZINBWAVE vignette (http://bioconductor. org/packages/zinbwave/).
Funding This research was supported in part by  StUDyS  grant  P7/06  from the  Interuniversity Attraction Poles (IAP) research network of the Belgian government (Belgian Science Policy)  and the  Multidisciplinary Research Partnership "Bioinformatics : from nucleotides to networks" of  Ghent University. KVDB is supported by a  Strategic Basic Research PhD  grant from the  Research Foundation, Flanders (FWO ;  1S 418 16N ). CS is supported by the  Forschungskredit of the University of Zurich , grant  FK-16-107. ML is supported by  National Institutes of Health (NIH)  grant  CA142538-08. DR and SD were supported by the  NIH BRAIN Initiative  (grant  U01 MH105979 , principal investigator:  John Ngai ). JPV was supported by the  French National Research Agency  (grant  ABS4NGS ANR-11-BINF-0001 ), the  European Research Council  (grant  ERC-SMAC-280032 ), the  Miller Institute for Basic Research in Science , and the  Fulbright Foundation.
Availability of data and materials The Islam dataset  [35]  was downloaded from the Gene Expression Omnibus with accession number GSE29087. The Trapnell dataset  [63]  was downloaded from the CONQUER repository  [30]  at http://imlspenticton. uzh. ch:3838/ conquer/. The Usoskin dataset  [64]  was downloaded from http://linnarssonlab. org/drg/. The 10x Genomics PBMC dataset  [62, 65]  was downloaded from https://s3-us-west-2. amazonaws. com/10x. files/samples/cell/pbmc3k/ pbmc3k_filtered_gene_bc_matrices. tar. gz. An R software package for our novel scRNA-seq simulation framework is available on the GitHub repository for this manuscript (https://github. com/ statOmics/zinbwaveZinger). All analyses and figures reported in the manuscript can be reproduced using code in this GitHub repository.
Our research approach followed systematic combining and abductive reasoning  (Dubois and Gadde, 2002; Timmermans and Tavory, 2012) , which is a mixture of deduction and induction. The research had several phases: Initially, we aimed to uncover the business modeling process. We collected preliminary data in the field and identified some intriguing notions and elements of experimentation processes in business modeling. Subsequently, we made a broad search of the literature, and reviewed articles about experimentation and modeling. During this literature review, a new question evolved about the forms and roles of experimentation in business modeling, and-with it-a new theoretical framework. The data revealed novel insights about how business modeling happens, and the roles and forms of experimentation in the process.
Data collection We collected data on the business modeling processes in DataScent and PortLab. To increase the study's validity, we used triangulation and several data sources  (Eisenhardt, 1989; Yin, 1984) ; we present these below. In-depth interviews. Semi-structured interviews were the primary data source, being the most appropriate method to obtain retrospective and real-time data about the experience of people who are directly and deeply involved  (Gioia et al. , 2013). We interviewed top managers from DataScent and PortLab, as they are involved in business modeling processes in their everyday work. We especially focused on the CEOs (who, in both cases, are also cofounders), as they have chief responsibility for the business modeling process. This follows  Langley et al. 's recommendation (2013)  to focus on how their experiences of particular individuals unfold over time, based on earlier experiences, interactions, and expectations.
We used  Gioia et al. 's methodology (2013) , and inductively coded the data for the experimentation roles and forms. We used software (Atlas. ti) to facilitate the coding. During the first part of the analysis, we examined the data and observed how the business modeling process actually takes place. We analyzed the components of the business models using Baden-Fuller and Mangematin's framework (  2013 ), which has four components: identifying customers; customer engagement; monetization; and value chain and linkages. This helped us to identify different business models in the two cases: two business models in DataScent, which we refer to as the "technology platform" and the "product" business models; and three in PortLab, which we refer to as the "patient," "hospital," and "lab" business models.
We examined and compared the business models at the various stages in order to assess the impact of experimentation and to understand its role in business modeling. We used several of  Langley's strategies (1999)  to make sense of process data (narrative strategy, visual mapping, and temporal bracketing). First, we used narrative strategy as a preliminary tool to describe all the processes, so we could observe the sequence of events. After completing the initial coding, we created chronological narratives for each case; we included the quotes from the data to support each event. This step helped to identify the processes and different experimentation projects that emerged. We sent these narratives to key informants, who confirmed our understanding of the business modeling process. This enhanced the study's reliability  (Yin, 1984).
Three challenging questions emerged for both business models: How could the company create value, for whom could it do so, and how could it be monetized? The issue was how to create value for the customer in a niche and undiscovered medical market. Apart from that, another potential way to create value-this time for the company-was to build synergy between the business models. The CEO's hypothesis was that this would happen if he built a database to which different technology users would contribute data, so improving usage for everyone. The main question raised was how people would use the device to create a database and additional value. Monetization was a big issue because some of the technology applications were new to the market so there were no previous pricing standards. The CEO and board collectively decided that the company would engage first in a technology platform business model, but retain the product business model as a secondary one to explore with one product line.
In PortLab, the CEO tested hypotheses about the patient and hospital business models through purposeful interaction with experts, potential customers, and partners. The CEO engaged in months of close interaction with patients and practitioners in order to answer questions about these business models; this resulted in a decision to abandon both. PortLab also experimented with purposeful interactions for the lab business model. It designed an offer that it pre-tested on a smaller scale, with one laboratory and with lower-level management. After this, the company was ready to test the model with higher-level management in this laboratory and in others. The second form of experimentation identified from the data is the experimental project. DataScent developed a mobile app as a means of interacting with customers, investigating how they would use the device to record smells, and identifying what kind of value database it could create.
This activity was a real-life experiment, as it included interaction with potential customers-both final users and industrial partners-in a real-life setting. The aim was to investigate how people were recording smells, how many people would be interested in this sort of activity, and what kind of value it would create for customers and for industrial partners. This experiment started in summer and autumn 2014, six months after the company began operating. It was a lower-risk experiment for the company, being inexpensive to implement. However, experimentation failed to answer the questions regarding the use and habits of smell recording, because the app itself did not work well and there were issues with the database. These problems resulted from the lack of clarity in the business model about the role of the database, which ultimately impacted the technological design.
This was an important decision and brought very promising feedback, which promoted the business model in several experimentation cycles-first through small-scale purposeful interactions and then through a larger-scale experimental project with multiple partners. Experimentation brought new insight for the business model, and new questions to test. In the experimental project, the company tested the business model on value creation for-and usage by-different users. This represented a crucial step in fine-tuning the business model before going to market. In DataScent, the CEO explained that even though the app did not provide the exact information needed about usage of the device, and the value of the database, it played a signaling role: It was this period, just three to six months after the creation of the company.
Acknowledgments The authors would like to thank the Editor and the two anonymous reviewers for their insightful comments and help with developing the paper. They are also grateful for the helpful input from all participants in the two case studies; from conveners and participants in the Business Model Days 2015 in Grenoble, EGOS 2016  Business Model Track , and Process Symposium 2016 pre-conference workshop; and from colleagues at  Grenoble Ecole de Management. This research has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  ( Marie Sklodowska-Curie Actions Grant  Agreement No. 676201 ) and  CHESS (Connected Health Early Stage Researcher Support System). The authors acknowledge funding received by the  French Agence nationale de recherche  in the context of the  Better Business Models project  ( ANR-13-SOIN-0001 ).
Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows.
The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.
The scientific community is progressively addressing this problem. Prestigious conferences (such as two major conferences from the database community, namely, VLDB 1  and SIGMOD 2  ) and journals (such as PNAS, Biostatistics  (Peng, 2009) , Nature  (Santori, 2016) , and Science  (Yaffe, 2015) , to name only a few) now encourage or even require published results to be accompanied by all the information necessary to reproduce them. While the reproducibility challenges have first been observed in domains where deluge of data were quickly becoming available (e. g. , Next Generation Sequencing data analyses), the problem is now present in many (if not all) communities where computational analyses and simulations are performed. In particular, the Systems Biology community is facing a proliferation of approaches to perform a large variety of tasks, including the development of dynamical models, complex simulations, and multiple comparisons between varying conditions of model variants.
, 2015). As a first outcome to foster model exchange and software interoperability, the SBML L3 package qual was developed  (Chaouiya et al. , 2013 (Chaouiya et al. , , 2015)). In this manuscript, we report the next phase of the CoLoMoTo efforts in the area of reproducibility in computational systems biology: The CoLoMoTo Interactive Notebook, which provides an easy-to-use environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks by seamlessly integrating various logical modeling software tools. The teams involved in CoLoMoTo, gathering around 50 researchers within 20 groups and laboratories, have produced various software tools for the qualitative modeling and analysis of biological networks. They are also involved in the development of novel computational methods and models.
This method article presents a collective effort to provide the community with a reproducibility-oriented framework combining software tools related to logical modeling. This framework combines the power of different approaches to ensure repeatability and to reduce the requirement of technical knowledge from users. The provided Docker image facilitates the stability of a contained environment needed for repeatable computational modeling and analyses. The framework includes a set of pre-installed tools from the CoLoMoTo community. On the other hand, specific binding and interfaces integrated in a Jupyter environment reduce the learning curve and improve accessibility. The use of this framework is demonstrated by a case study in a companion protocol article, which consists in a thoroughly annotated Jupiter notebook  (Levy et al.
, 2018)   5. The method article is structured as follows. Section 2 provides a brief introduction to qualitative models of biological networks and to their analyses. Section 3 describes the main components (Docker image, Python programming interface, Jupyter interactive web interface) of our framework to facilitate the access to CoLoMoTo software tools, a prime prerequisite for the reproducibility of the computational analyses. Section 4 illustrates how our framework can address several challenges related to the reproducibility of computational analyses, ranging from the repeat of a sequence of analyses in the exact same software environment, to the use of alternate methods to reproduce a result. Finally, section 5 provides an introductory guide on how to use the new framework, and section 6 discusses possible extensions.
From a biological point of view, computing attractors is generally particularly relevant. The presence of multiple attractors can represent alternative cell fates (such as cell differentiation states), while cyclic attractors further represent periodic behaviors (such as cell cycle or circadian rhythms). The computation of attractors is addressed by different software tools, such as BIOLQM (Naldi, in review 6 ), GINSIM  (Naldi et al. , 2018) , PINT  (Paulevé, 2017) , BOOLSIM  (Garg et al. , 2008) , BOOLEANNET  (Albert et al. , 2008) , PYBOOLNET  (Klarner et al. , 2017) , and BOOLNET  (Müssel et al. , 2010). Simulations allow capturing the states reachable from a given (set of) initial state(s). They can consist of random walks in the complete state transition graph, take into account updating priority schemes to distinguish fast versus slow processes and thereby obtain a simpler state transition graph  (Fauré et al.
ACCESSIBILITY OF COLOMOTO SOFTWARE TOOLS The CoLoMoTo Interactive Notebook aims at offering a unified environment for accessing a range of complementary software tools for the analysis of qualitative models of biological networks. To achieve such a goal, our framework relies on three complementary technologies. First, we use the Docker system to provide images of preinstalled selected CoLoMoTo software tools, thus reducing significantly the burden of installing individually each software tool. The software installed within Docker images can be executed on GNU/Linux, macOS, and Microsoft Windows, FIGURE 1 | Feature matrix and characteristics of a range of software tools devoted to the qualitative modeling and analysis of biological networks: CELLCOLLECTIVE  (Helikar et al. , 2012) ; GINSIM  (Naldi et al.
, 2018) ; BIOLQM (Naldi, in review 6 ); MABOSS  (Stoll et al. , 2017) ; PINT  (Paulevé, 2017) ; NUSMV  (Cimatti et al. , 2002) ; BOOLSIM  (Garg et al. , 2008) ; BOOLEANNET  (Albert et al. , 2008) ; PYBOOLNET  (Klarner et al. , 2017) ; BOOLNET  (Müssel et al. , 2010) ; CELLNOPT  (Terfve et al. , 2012) ; CASPOTS  (Ostrowski et al. , 2016). The current CoLoMoTo Docker (2018-03-31) ships the software indicated with a bold font and a light blue background. "Model repository" refers to searchable databases of models; "Model edition" refers to the features related to creating and modifying a qualitative model, where "ab initio" refers to the interactive model building from scratch, and "transformations" refers to operations such as mutations, Booleanization, model reduction, etc. "Topological analysis" refers to the extraction of features from the regulatory graph, such as the different feedback cycles, graph theory measures, etc.
Then, we developed a collection of Python modules to provide a unified interface to the features of the selected software tools. The Python modules allow to parameterize and execute the different analyses, and fetch their results, which can then be further processed, including by a different tool through its respective Python module. This uniform Python interface is particularly relevant in the Jupyter web interface  (Ragan-Kelley et al. , 2014) , where it allows editing executable notebooks on qualitative biological networks by seamlessly combining different software tools.
The CoLoMoTo Docker Image Overall, we witness a growing ecosystem of software tools based on different technologies and offering a wide range of complementary features. Noteworthy, these tools typically rely on tailored formalisms and settings, which enable specific methods but at the same time affect the results. One obvious example is the consideration of a specific updating mode, as synchronous and asynchronous dynamics may differ extensively. Furthermore, to address increasingly large networks, many tools rely on advanced data-structures and resolution methods, which are implemented in dedicated software libraries. The distribution of these tools then become challenging, as they rely on numerous dependencies, often difficult to install or available only for a specific operating systems (most of the time GNU/Linux).
The Docker container technology allows to circumvent such distribution issues by providing a mean to supply pre-installed and fully configured software environments in so-called Docker images. On GNU/Linux, the execution of a Docker image consists mainly in executing the software in an isolated environment, requiring no operating system virtualization. Therefore, the overhead of using Docker on GNU/Linux is close to zero. A Docker image can also be executed on macOS or Microsoft Windows without any modification. On these operating systems, Docker relies on virtualization technologies, which are relatatively lightweight and result in limited performance loss on recent hardware. The current CoLoMoTo Docker image colomoto/colomoto-docker:2018-03-31 contains the following pre-installed software for the logical modeling and analsyis of biological networks: GINSIM  (Naldi et al.
, 2018) , BIOLQM  (Naldi, in review)   6 , CELLCOLLECTIVE  (Helikar et al. , 2012) , MABOSS  (Stoll et al. , 2017) , PINT  (Paulevé, 2017) , and NUSMV  (Cimatti et al. , 2002). The CoLoMoTo Docker image then provides access to these tools without requiring any installation step beside installing Docker 7. For instance, the Docker image can be used in association with a workflow manager to chain and run a series of software functionalities. Supplementary File "SnakeMake" provides an example of SNAKEMAKE workflow relying on GINSIM and NUSMV. An important challenge is the maintenance and extendibility of such Docker images to reduce the complexity of upgrading or adding software tools with their respective dependencies. To that aim, we require that each software tool is independently packaged for GNU/Linux using the Conda package manager 8.
A Unified Interface for Calling and Chaining Tools With Python The software tools considered for the CoLoMoTo Docker image present different interfaces: CELLCOLLECTIVE is a web application, GINSIM has a graphical user interface along with a scripting interface, BIOLQM has a command line and a scripting interface, PINT has a command line and a Python interface, MABOSS has a command line interface. GINSIM, CELLCOLLECTIVE and BIOLQM support the SBML-qual format, while BIOLQM provides the conversion of a standard SBML-qual model into PINT or MABOSS model formats, thereby enabling the exchange of models between all these tools. The recourse to different interfaces complicates the design of a model analysis combining multiple tools. To address this issue, we have developed a Python interface for each of the tools embedded in the CoLoMoTo Docker image, which greatly ease the execution of different tool functionalities, fetch the results, and use these as input for other executions.
Each tool comes with a dedicated Python module, providing a set of functions to invoke the underlying software tool appropriately. Therefore, from a single Python shell, one can invoke and chain analyses performed by different tools. This can be seen as an improved command line interface, greatly enhanced by the use of intermediate Python objects. Such an approach also promotes the use of standard Python data-structures to store objects such as model states or graphs, which can then be processed by common Python libraries, e. g. , PANDAS 10  or NETWORKX  11. Hereafter, we give an overview of the resulting Python programming interface, focusing on the general model input mechanism and the main features implemented for each of the software tools.