dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION The bru2bids workflow presented herein is a significant first step in rendering data in the Bruker ParaVision standard automatically interpretable for high-level analysis pipelines. This is done by repositing the ParaVision data according to the BIDS standard, which offers superior legibility, as well as integration with community analysis tools, specifically with tools adapted from human fMRI. The BIDS reposited data form can serve as a mere intermediary, facilitating data usage with standardized workflows, but can also be used in and of itself as raw data recourse-if data management expediency is prioritized over the larger pool of accessible information in the full ParaVision standard. To demonstrate and persistently track compliance, we release a versioned archive of Bruker ParaVision testing data, diverse in terms of both animal species and acquisition protocols.
We test the performance of the workflow on the dataset, and report compliance with the target standard. These demonstrated capabilities of the workflow are rendered accessible to the community by procedural instructions addressed to Bruker MRI scanner operators. Additional accessibility is conferred by a detailed walk-through, which allows custodians of Bruker ParaVision data to render preexisting records compatible as a workflow input. Further, we describe the general principles of the software implementation, which in conjunction with the documentation internal to the software enable collaborators to inspect, debug, augment, or create derivations based on our work. The demonstrated performance of this workflow, its position at the transition from the most popular small animal MRI acquisition format into the most popular MRI data sharing format, as well as its transparent free and open source nature, make the SAMRI's bru2bids a strong foundation for the rapid and collaborative improvement of fMRI data analysis methodology.
Data from each scale are collected with different frequencies, proportional to their respective time scale, so that observations are more frequent for the fast than for the slow scale. In order for the observations of the slow scale system's component to monitor its variability, the observational interval has to be of order ∆t x = O( -1 ). The fast scale observational interval has to be shorter than the slow scale one, ∆t z ≤ ∆t x , and we stipulate for convenience that ∆t x = K∆t z , with K ∈ N, meaning that every ∆t x both scales are simultaneously observed. Note that when ∆t z = O(1) the solution of the slow system, x(t), can be considered approximately constant in the interval t ∈ [t k , t k + ∆t z ]. The model defined by Eq. (  1 ) is used to assimilate recursively data y z every ∆t z , and data y = (y x , y z ) T whenever t k is a multiple of ∆t x.
The EnKF is used to perform sCDA and the results that follow refer to this case only. We have also performed experiments using wCDA and the results, not shown, indicate overall lower skills than sCDA. Experiments are performed with varying ensemble size, N , as well as atmosphere and ocean observational intervals, ∆t atm and ∆t ocn. Simulated observations are sampled from a trajectory, solution of MAOOAM, that is taken to represent the truth with respect to which we compute the root-mean-squareerror (RMSE), as in standard twin experiments. Observational error is simulated by adding Gaussian random noise, and the model-to-data relation reads: y k = y atm k = H atm (x k , z k ) + atm k when mod (t k , ∆t ocn ) = 0, (6) and y k = y ocn k y atm k = H ocn (x k , z k ) + ocn k H atm (x k , z k ) + atm k when mod (t k , ∆t ocn ) = 0,  (7)  with H ocn : R mocn+matm → R docn and H atm : R mocn+matm → R datm being the observational operators, d ocn ≤ m ocn and d atm ≤ m atm , and ∆t ocn = K∆t atm , K ∈ N.
Acknowledgements The numerical experiments, and their analysis, were performed during the internship that Maxime Tondeur spent at NERSC and RMI in 2017, as part of his Master at  École des Mines Paris. The internship was funded by the  Nordic Centre of Excellence EmblA of the Nordic Countries Research Council ,  NordForsk. Alberto Carrassi  has been funded by the  Trond Mohn Foundation  under the project number  BFS2018TMT0  and by the  Natural Environment Research Council  ( Agreement PR140015  between  NERC  and the  National Centre for Earth Observation). Stèphane Vannitsem  acknowledges partial support by the  Belgian Science Policy Office  under contract  BR/165/A2/Mass2Ant. CEREA is a member of the  Institut Pierre-Simon Laplace (IPSL).
Projections of climate change to 2100 show an accelerating trend, implying that more severe damages are expected after 2050. However, we limited our analysis to 30 years, as this is the relevant horizon for policies aimed at strengthening the seed sector in developing countries. We used a discount rate of 5%, reflecting the public good nature of the proposed policy (help vulnerable farmers adapt), for which the use commercial discount rates is not appropriate. We use two types of input data: nationally representative farm household survey datasets and projections of crop prices and yields under alternative climate scenarios. Geo-referenced survey data used consists of panels for Malawi (2281 households) and Tanzania (2213 households) with two waves of sampling (Figs. 1  and 2 ). For Malawi, farm household-level data were from the nationally representative sample from the Third Integrated Household Survey (IHS3) for 2010/2011 and 2013, implemented by the Malawi National Statistics Office.
For Tanzania, farm household-level data were from the nationally representative sample from the 2008/2009 and 2010/2011 waves of the Tanzania National Panel Survey (TZNPS). Both datasets were matched to historical measures of temperature and rainfall variability by the FAO-EPIC team. The crops available for selection in the optimisation model were based on those present in the farm household survey datasets. The main crop cultivated in both countries was maize, with 79% and 49% of households cultivating this crop in Malawi and Tanzania, respectively. Maize was followed by legumes in both countries: 19% of households planted groundnut in Malawi and 20% of households planted beans in Tanzania. There were variations in crop proportions among agro-ecological zones (see Tables  A4  and A5  in Supplementary Information for more details).
Price and yield projections for 2010-2050 for the crops of interest were generated through simulations under alternative representative concentration pathways (RCP)  (Moss et al. 2008)  with no CO 2 fertilization using the MAgPIE/LPJml model  (Popp et al. 2017; Lotze-Campen et al. 2008; Bondeau et al. 2007). MAgPIE results for yields and prices were obtained for 4 global circulation models (GCMs). The GCMs used were: (1) Geophysical Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M); (2) Hadley Global Environment Model 2 Earth System (HadGEM2-ES); (3) Institut Pierre Simon Laplace Earth System Model for the 5th IPCC report (IPSL-CM5A); and (4) Norwegian Earth System Model (NorESM1-M). The gridded data obtained from the MAgPIE/LPJml model were at a spatial resolution of 0. 5°. Each crop was simulated with and without irrigation for all global grid cells for all RCP-GCM combinations.
Yield values were extracted for the pixels corresponding to Malawi (1419 points) and Tanzania (10,547 points). Price projections relative to 2010 were uniform for the whole African region, and they were adjusted by AEZ based on price data from the country datasets prior to use in our simulations. We used only non-irrigated yields in this analysis, as the study is based on dryland systems, and options for investment in irrigation infrastructure were not considered. The model described in Eqs. (  1 )-(  10 ) was solved for the 4 climate scenarios and using projections from the 4 GCMs listed above. We simulate a policy measure intended to generate seed breeding, production, and distribution capacity as well as provide incentives to smallholders to adopt improved and adapted seeds at enhanced rates.
Evaluating climate-resilient seeds Seeds that are drought-and heat-resistant can increase yields by up to 25% under climate patterns expected in Africa by 2050 with RCP 8. 5, compared with expected yields with current seeds  (Islam et al. 2016 ). We explore the potential benefits of farmers adopting these adapted seeds under three different scenarios (Table  3 ). First, an optimistic scenario where these Projections were generated using four GCMs and a grid of 5-arcmin pixels. The mix of crops differs between countries based on the farm household datasets used in the analysis *Representative concentration pathways (RCP) are used by the IPCC as the new standard to represent a range of climate scenarios through time. The numbers (2. 6, 6. 0, and 8. 5) refer to radiative forcing, with higher numbers representing more severe warming  (Moss et al.
In this regard, it is important to have a diversity of cultivars available to breed for desirable traits. The analysis focuses on the main crops present in the country datasets (Table  1 ), with maize being the most prominent. The optimal solution at the farm level involves crop mixes that maximize utility for risk-averse farmers. There are other promising crops for climate adaptation in Africa that were not included in the analysis (such as cowpea and millet) because of their absence in the datasets. Maize will be hit hard by climate change in low latitudes  (Mbow et al. 2019 ), and at the same time, maize continues to be of critical importance in diets, with more than a 30% contribution to the calories consumed by the population of sub-Saharan Africa. There is a reluctance by farmers and consumers to shift to other staple crops.
This reflects the case of low input cropping systems where little scope exists for crops to express CO 2 yield benefits because of other constraints to crop growth. Also, estimating the potential cost of inaction under the worst scenario (RCP8. 5 with no CO 2 fertilization) provides a useful starting point for evidence-based policy analysis using large household datasets. The probability of this scenario coming to pass can be estimated separately and adjusted over time as international negotiations and emission outcomes progress. This type of analysis can help countries plan to prevent the worse-case outcomes. Seeds that are heat-and drought-resistant and high yielding are a key tool for adaptation of vulnerable farmers in sub-Saharan Africa (SSA) and ultimately for achieving SDG2. New seeds represent relatively straightforward adaptation: farmers know the crops and varietal changes generally present few challenges, provided there are no issues around acceptability with respect to taste, color, or other plant attributes, for example  (Thornton et al.
We are grateful to the  EPIC team at FAO  for providing data and support;  Aslihan Aslan  and  Caterina Sucameli  provided clean datasets for Malawi and Tanzania. PKT acknowledges support provided to the  CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)  from  CGIAR Fund Donors , and through bilateral funding agreements (please see ccafs. cgiar. org/donors). MH and BH acknowledge support from the  CSIRO Science Leaders Programme  and the  FACCE/JPI Belmont Forum  funded  DEVIL  project ( Delivering Food Security on Limited Land ).
Figures are in 2010 US Dollars at a discount rate of 5%. *Agro-ecological zones are as classified by IFPRI -Sebastian, K. (2015). Agro-ecological zones of Africa (Publication no. hdl/1902. 1/22616) from Harvard Dataverse http://hdl. handle. net/1902. 1/22616 + These costs were estimated from Eq. (2) as -∑ j.
Background: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology.
Pitfalls and technical challenges Sequencing sensitivity and normalization. With each new protocol comes a host of new technical problems to overcome. The first wave of software utilities to deal with the analysis of single-cell datasets were statistical packages, aimed at tackling the issue of "dropout events" during sequencing, which would manifest as a high prevalence of zero-entries in >80% of the feature count matrix. These zeroes were problematic because they could not be trivially ignored as their presence stated either that the cell did not produce any molecules for that transcript or that the sequencer simply did not detect them. Normalization techniques originally developed for bulk RNA-seq had to be adapted to accommodate for this uncertainty, and new ones were created that harnessed hurdle models, data imputation via manifold learning techniques, or by pooling subsets of cells together and building general linear models  [4].
The burgeoning software ecosystem Since its conception, several different packages and many pipelines have been developed to assist researchers in the analysis of single-cell RNA sequencing (scRNA-seq)  [5, 6]. Most of these packages were written for the R programming language because many of the novel normalization methods developed to handle the dropout events depended on statistical packages that were primarily R-based  [7]. Stand-alone analysis suites emerged as the different authors of these packages rapidly expanded their methods to encapsulate all facets of the single-cell analysis, often creating compatibility issues with previous package versions. The Bioconductor repository provided some muchneeded stability in this regard by hosting stable releases, but re-searchers were still prone to building directly from repository sources in order to reap the benefits of new features in the upstream versions  [8, 9].
Rise of 10x Genomics 10x launch. In 2015, 10x Genomics released their GemCode product, which was a droplet-sequencing-based protocol capable of sequencing tens of thousands of cells with an average cell quality higher than other facilities  [14]. This unprecedented level of throughput steadily gained traction amongst researchers and start-ups seeking to perform single-cell analysis, and thus 10x datasets began to prevail in the field. 10x analysis software. 10x Genomics provided software that was able to perform much of the pre-processing, and provided feature-count matrices in a transparent HDF5-based format that provided a means of efficient matrix storage and exchange, and conclusively removed the restriction for downstream analysis modules to be written in R. ScanPy, a popular alternative.
The ScanPy suite  [15] , written in Python using its own HDF5-based AnnData format, became a valid alternative for analysing 10x datasets. The Seurat developers had similar aspirations and soon adopted the LOOM format, another HDF5 variant. However, the popularity of ScanPy increased as it began to integrate the methods of other standalone packages into its codebase, making it the natural choice for users who wanted to achieve more without compromising on compatibility between different suites  [9].
Solutions in the cloud Accessible science. As the size of datasets scaled, so did the computing resources required to analyse them, in terms of both processing power and storage. Galaxy is an open source biocomputing infrastructure that exemplifies the 3 main tenets of science: reproducibility, peer review, and open access-all freely accessible within the web browser  [16]. It hosts a wide range of highly cited bioinformatics tools with many different versions and enables users to freely create their own workflows via a seamless drag-and-drop interface. Reproducible workflows. Galaxy can make use of Conda or Containers to set up tool environments to ensure that the bioinformatics tools will always be able to run, even when the library dependencies for a tool have changed, by building tools under locked version dependencies and bundling them together in a self-contained environment  [17].
These environments provide a concise solution for the package version instability that plagues scRNA-seq analysis notebooks, in terms of both reproducibility and analysis flexibility. A user could keep the quality control results obtained from an older version of ScanPy whilst running a newer ScanPy version at the clustering stage to reap the benefits of the later improvements in that algorithm. By allowing the user to select from multiple versions of the same tool, and by further permitting different versions of the tools within a workflow, Galaxy enables an unprecedented level of free-flow analysis by letting researchers pick and choose the best aspects of a tool without worrying about the underlying software libraries  [18]. The burdens of software incompatibility and choice of programming language that plagued the scRNA-seq analysis ecosystem before are now completely removed from the user.
User-driven custom workflows. Analyses are not limited to one pipeline either because the datasets that are passed between tools can easily be interpreted by a different tool that is capable of reading that dataset. In the case of scRNA-seq, Galaxy can convert between CSV, MTX, LOOM, and AnnData formats. This interexchange of modules from different tools further extends the flexibility of the analysis by again letting the user decide which component of a tool would be best suited for a specific part of an analysis. Training resources. Galaxy also provides a wide range of learning resources, with the aim of guiding users step by step through an analysis, often reproducing the results of published works. The teaching and training materials are part of the Galaxy Training Network (GTN), which is a worldwide collaborative effort to produce high-quality teaching materials to educate users in how to analyse their data, and in turn to train others on the same materials via easily deployable workshops backed by monthly stable releases of the GTN materials  [19].
One common pitfall at this very first stage is estimating how many cells to expect from the FASTQ input data, and this requires 3 crucial pieces of information: which reads contain the barcodes (or precisely, which subset of both the forward and reverse reads contains the barcodes); of these barcodes, which specific ones were actually used for the analysis; and how to resolve barcode mismatches/errors. Barcode estimation. Naive strategies involve using a known barcode template and querying against the FASTQ data to profile the number of reads that align to a specific barcode, often employing "knee" methods to estimate this amount  [22]. However, this approach is not robust; certain cells are more likely to be overrepresented compared to others, and some cell barcodes may contain more unmappable reads compared to others, meaning that the metric of higher library read depth is not necessarily correlated with a better-defined cell.
One-click pre-processing Quantification with Cell Ranger. 10x Genomics simplified the single-cell RNA package ecosystem by using a languageindependent file format and streamlining much of the barcode particularities with their Cell Ranger pipeline, allowing researchers to focus more on the internal biological variability of their datasets  [23]. Quantification with STARsolo. The pre-processing workflow (titled "10X StarSolo Workflow") in Galaxy uses the RNA STARsolo utility as a drop-in replacement for Cell Ranger, not only because is it a feature update of the already existing RNA STAR tool in Galaxy but because it boasts a 10-fold speedup in comparison to Cell Ranger and does not require Illumina lane-read information to perform the processing  [24, 25]. Other approaches. The pre-processing workflows for these "1-click" solutions consume the same datasets and yield approximately the same count matrices by following similar modes of barcode discovery and quantification.
Downstream workflows Common stages of analysis. The downstream modules are defined by the 5 main stages of downstream scRNA-seq analy- sis: filtering, normalization, confounder removal, clustering, and trajectory inference. There are 3 workflows to aid in this process (2 of which are shown in Fig. 2 ), each sporting a different wellestablished scRNA-seq pipeline tool. Quality control with Scater. The Scater pipeline follows a visualize-filter-visualize paradigm, which provides an intuitive means to perform quality control on a count matrix by use of repeated incremental changes on a dataset through the use of principal component analysis and library size-based metrics  [30]. Once this pre-analysis stage is complete, the full downstream analysis (comprising the 5 stages mentioned above) can be performed by workflows based on the following suites: RaceID and ScanPy.
Downstream analysis with the RaceID suite. RaceID was developed initially to analyse rare cell transcriptomes whilst being robust against noise and thus is ideal for working with smaller datasets in the range of 300-1,000 cells. Owing to its complex cell lineage and fate prediction models, it can also be used on larger datasets with some scaling costs. Downstream analysis with the ScanPy suite. ScanPy was developed as the Python alternative to the innumerable packages for scRNA-seq that were based on R, which was the dominant language for such analyses, and it was one of the first packages with native 10x Genomics support. Since then it has grown substantially and has been re-implementing much of the newer Rbased methods released in BioConductor as "recipe" modules, thereby providing a single source to perform many different types of the same analysis.
Filtering Cell and gene removal. During the filtering stage, the initial count matrix removes low-quality or unwanted cells using typical parameters such as minimum gene detection sensitivity and minimum library size, and low-quality genes are also removed under similar metrics, where the minimum number of cells for a gene to be included is decided. The Scater pre-analysis workflow can also be used here to provide a principal component analysisbased method of feature selection so that only the highly variable genes are left in the analysis. Disadvantages of filtering. There is always the danger of overfiltering a dataset, whereby setting overzealous lower-bound thresholds on gene variability can have the undesired effect of removing essential housekeeping genes. These relatively uniformly expressed genes are often required for setting a baseline to establish a threshold to distinguish the more desired differentially expressed genes.
Confounder removal Regression of cell cycle effects. Other sources of variability stem from unwanted biological contributions known as confounder effects, such as cell cycle effects and transcription. Depending on the stage of the cell cycle at which a cell was sequenced, 2 cells of the same type might cluster differently because one might have more transcripts as a result of it being in the M-phase of the cell cycle. Library sizes notwithstanding, it is the variability in specific cell cycle genes that can be the main driving factor in the overall variability. Thankfully, these effects are easy to regress out, and we replicate an entire stand-alone ScanPy workflow dedicated to detecting and visualizing the effects based on the original notebook  [32]. Transcriptional bursting. The transcription effects are harder to model because these are semi-stochastic and remain not well understood.