dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability. The last version of the MUNICH source code is available online at https://doi. org/10. 5281/zenodo. 4168984 (Kim et al. , 2022)  and https://github. com/cerea-lab/munich (last access: 5 January 2022). Data availability. For the three 2D canyons considered in the present study, the mesh, the source code, and the XML setup file allowing the reproduction of the CFD results using Code_Saturne version 6. 0 are available online at https://gitlab. enpc. fr/alice. maison/tree_parametrization (last access: 3 December 2021) and at https://doi. org/10. 17632/fzfrjsz3mv. 2  (Maison and Flageul, 2021)  under the GNU GPL2. 0 license.
In previous work ("From signatures to monads in UniMath"), we described a category-theoretic construction of abstract syntax from a signature, mechanized in the UniMath library based on the Coq proof assistant. In the present work, we describe what was necessary to generalize that work to account for simply-typed languages. First, some definitions had to be generalized to account for the natural appearance of non-endofunctors in the simplytyped case. As it turns out, in many cases our mechanized results carried over to the generalized definitions without any code change. Second, an existing mechanized library on -cocontinuous functors had to be extended by constructions and theorems necessary for constructing multi-sorted syntax. Third, the theoretical framework for the semantical signatures had to be generalized from a monoidal to a bicategorical setting, again to account for non-endofunctors arising in the typed case.
This uses actions of endofunctors on functors with given source, and the corresponding notion of strong functors between actions, all formalized in UniMath using a recently developed library of bicategory theory. We explain what needed to be done to plug all of these ingredients together, modularly. The main result of our work is a general construction that, when fed with a signature for a simply-typed language, returns an implementation of that language together with suitable boilerplate code, in particular, a certified monadic substitution operation.
Contributions We present a fully formalized mathematical framework for simply-typed syntax relying on a categorical construction of inductive families as initial algebras. In this way, we avoid meta-programming and complex nested inductive types, and instead work completely internally to UniMath. We generically equip the constructed syntax with a monadic substitution operation. Furthermore, we provide a bicategorical analysis of the involved notions, motivated by the generalization of strength needed to encompass multi-sorted languages. All results in this paper have been formalized in UniMath. The code is integrated into the UniMath library  [63]. For documentation, we refer to a specific version of UniMath, available in commit c26d11bef0b4d4c226ca854d8de340d684c2a10a. The HTML documentation derived from this version is hosted online.
Such frameworks take a signature specified in some domain-specific language, e. g. , as a Haskell data type, and return a library of computer code for some proof checker such as Coq. 1  To this category belong the tools DBGen  [51] , Ott  [55] , LNGen  [64] , Autosubst  [54]  and Autosubst2  [56]. These are different from our approach in that they typically rely on some form of meta-programming as the notion of signature is external. The second category consists of work building a library of generic functions and reasoning principles for (typed) abstract syntax. The library NomPa  [52, 53]  falls into this category, as does GMETA  [41]. In this line of work, there are several modules called signatures, providing an interface for signatures. However, there is not a fixed mathematical definition of a signature, making them less suited for general mathematical study of syntax and more focused on convenience for specifying and working with a specific language.
Acknowledgments We are grateful to  Vladimir Voevodsky  for many discussions on the subjects of multi-sorted term systems and the intended applications to type theory. We thank  Peter LeFanu Lumsdaine  for suggesting and formalizing an improvement to one of our proofs. Anders Mörtberg  was supported by the  Swedish Research Council (SRC, Vetenskapsrådet)  under Grant No. 2019-04545. This material is based upon work supported by the  National Science Foundation  under agreement No. DMS-1128155  and  CMU 1150129-338510. This work has been partly funded by the  CoqHoTT ERC  Grant  637339.
In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.
Kinetoplastids Trypanosoma spp. The genome sequencing of T. brucei brucei  (Berriman et al. , 2005)  was followed by remaining T. brucei subspecies, T. b. gambiense  (Jackson et al. , 2010) , T. b. rhodesiense  (Sistrom et al. , 2016) , T. b. evansi  (Carnes et al. , 2015) , and T. b. equiperdum  (Hebert et al. , 2017; Davaasuren et al. , 2019). These genomes are ~32Mb in size and comparisons of these datasets have shown high synteny, large sequence homology and rare segmental duplications. However, these sequences, together with additional laboratory-adapted strains  (Cross et al. , 2014)  and population isolates  (Sistrom et al. , 2014) , have highlighted quite considerable diversity within the subtelomeres. The subtelomeres harbor multiple multi-copy gene families, of which the variant surface glycoproteins (VSG) are the most prominent.
Comparative analyses of the genome sequences of T. brucei, T. congolense and T. vivax, have shown that each species has distinct mechanisms of generating antigenic diversity  (Jackson et al. , 2012; Silva Pereira et al. , 2020)  and thus have different strategies for establishing chronic infections. These genome sequencing projects have also allowed the determination of the cell surface phylome, a database of genes encoding cell-surface genes and their evolutionary relationships within the main African trypanosome species  (Jackson et al. , 2013). Moreover, whole genome sequencing of clinical isolates from Human Sleeping Sickness patients has shown that disease relapse results from ineffective parasite clearance by melarsoprol  (Richardson et al. , 2016). On a larger scale, studies of population genomics have shown the importance of sexual replication in African trypanosome evolution.
Challenges and Accessibility A large bottleneck in the field of genomics has been the lack of analytical power to deconvolute complex data in a highthroughput manner. However, we have gathered a considerable set of computational tools that have streamlined the analysis of big data from parasite genomes. One of the most valuable platforms in parasitology is the VEuPathDB (https:// veupathdb. org/), which integrates big data repositories across all 'omics' and multiple analytical and visualization tools. VEuPathDB has made an enormous impact on how parasitologists and vector biologists perform data mining. The specific impact and importance of VEuPathDB will be discussed in detail in the last section of this work. Nevertheless, there are other, more specialized tools include Companion, a web-based annotation tool  (Steinbiss et al.
, 2018). RNA-seq was also employed to improve the annotations of the C. parvum and C. hominis reference genomes  (Isaza et al. , 2015; Baptista et al. , 2022). Analysis has yet to be performed of these data to compare the transcriptomes of the oocyct stages from each species, to the best of our knowledge. Exampling the use of older datasets, recently C. parvum ESTs  (Wakaguri et al. , 2009; Warrenfeltz and Kissinger, 2020)  were mined to reveal extensive microRNAs  (Ahsan et al. , 2021)  and RNA-seq to locate lncRNAs  (Li et al. , 2020). The result is a transcriptomic map descriptive of the system in question, which in parasitology can reflect the parasite's complete life cycle (expressed as arrows). Individual parasite transcriptomes of the same life cycle form (single points) are positioned close together and, if captured, cells undergoing differentiation between different forms are positioned between the cell type clusters.
For example, when only a few transcriptomes are available, or only those containing fewer transcripts per cell, mapping them to a high quality reference can identify their detailed position in the life cycle. 4) Transcriptomes of different genetically perturbed parasites, varied strains and even different species can also be mapped to the reference cell atlas through data integration methods. This allows detailed comparisons between datasets and across several cell types. Figure created with BioRender. com.
Completing the Life Cycles Cell atlases of the Plasmodium life cycle are a highly valuable resource providing the transcriptomic signatures of each life cycle form as well as cells differentiating between forms. scRNAseq was critical for gaining this level of resolution, as multiple transition steps occur asynchronously across the population and some life cycle forms are rare and only found as a sub-population which are difficult to isolate without marker genes. scRNAseq datasets (current and future) can provide a wealth of information including: identification of novel marker genes; dynamic gene expression patterns identifying transcripts peaking in specific cell types; and variation between cell types to identify developmental regulators (summarized in Figure  5 ). High quality cell atlases can also be used as a reference for other query single cell transcriptomes, for example, of a genetically altered parasite line, clinical samples or alternative species or strains.
, 2021). One such technology, Visium Spatial Gene Expression from 10x Genomics (https://www. 10xgenomics. com/products/ spatial-gene-expression), uses slides tiled with spots of adaptor oligos for RNA capture, where each spot has a specific barcode similar to scRNA-seq. When a tissue sample is laid over the slide it is imaged with microscopy and then the extracted RNA is barcoded according to its' position within the tissue. Thus, each transcriptome can be spatially organised. Although the size of each barcoded spot (currently 55 µm) is larger than Kinetoplastids and Apicomplexans, this level of resolution will likely have a huge impact on our understand of parasitic life cycles within tissue niches and host responses. Transcriptomics datasets could answer many key questions in the field, such as: how flexible is the African Trypanosome life cycle  (Guegan and Figueiredo, 2021; Lisack et al.
Data Integration and Comparison Integration of multiple datasets would be highly impactful. For example, several scRNA-seq studies have analyzed different stages of the T. brucei life cycle. Despite these all using a variety of methods, the raw data could be integrated as bioinformatic methods improve  (Argelaguet et al. , 2021)  to provide at least a particle life cycle atlas, as demonstrated in the Malaria field. Population-based transcriptomics constitute a highly significant bank of data which, with upgraded analysis methods, could be combined, analysed and compared to gain significant insight into these pathogens' biology. Here, we discuss only unperturbed parasite data, but these comparisons can clearly be extended to compare experimentally manipulated parasites. Lastly, "multi-omic" data integration would be hugely valuable to link transcript levels to protein levels and genomic features  (Subramanian et al.
QUANTITATIVE PROTEOMICS While mass spectrometry was being used in the 1990s for protein identification in parasitology, it was not until the early 2000s, once the respective genomes were published, that proteome datasets were derived. Quantitative proteomics remains an active field, as the advancement of mass analyzers has given rise to more sensitive mass spectrometers allowing for identification and quantification of low abundant ions in complex samples. These studies can generate large quantitative datasets where one can identify post-translational modifications, drug targets, life cycle differences, organellar compositions, among other applications. Quantitative proteomics can be split into relative quantitation or absolute quantitation. In the Kinetoplastid and Apicomplexan fields, relative quantitation proteomics is more commonly used as seen by the number of publications (Figure  6 ).
Apicomplexans Plasmodium spp. Although Plasmodium undergoes a complex life cycle in multiple hosts, most life stages are accessible enough to obtain sufficient material for proteomics, as seen with the hundreds of 'protein expression' datasets available on PlasmoDB  (Aurrecoechea et al. , 2009). There are 5300 predicted proteins in P. falciparum  (Hall et al. , 2005). Using SILAC, Nirmalan et al. (Nirmalan et al. , 2004)  effectively used isoleucine as their heavy isotype to quantify protein levels across the blood stages of P. falciparum. Isoleucine was the amino acid of choice because it was not made de novo from parasites or scavenged from the host, but efficiently taken up. Additionally, it is an abundant amino acid in P. falciparum, which allows for labeling to be present in most of the tryptic peptides  (Nirmalan et al.
This in turn governs major sporozoite life events in both, mosquito and mammalian hosts. Additionally there are studies using host blood plasma samples to study host-pathogen interactions in patients with P. falciparum and P. vivax. Kumar et al. (2020)  identified biomarkers for malaria severity using TMT labeling with LC-MS/MS. They found an upregulation of cell-to cell adhesion-related host proteins in P. falciparum infections and not in P. vivax. This study generated a large dataset of infected host blood plasma data that has been deposited to the ProteomeXchange Consortium via the PRIDE partner repository.
Toxoplasma gondii There have been over 20 proteomic studies with available datasets on ToxoDB  (Kissinger et al. , 2003). Some examples include using LFQ to develop a bradyzoite proteomic profile  (Garfoot et al. , 2019) , SILAC to create the phosphoproteome  (Treeck et al. , 2014; Beraki et al. , 2019) , and using LC-ESI-HDMS (liquid chromatography, electrospray ionization, high definition mass spectrometry) for absolute quantification of the secretome of tachyzoites  (Ramıŕez-Flores et al. , 2019). To identify differences across tachyzoites, bradyzoitecontaining cysts, and sporulated oocysts, Wang et al. used iTRAQ with LC-MS/MS and found 6285 proteins across the 3 stages, with hundreds being differentially expressed  (Wang et al. , 2017). Most recently, a study using hyperLOPIT, a method that uses ultracentrifugation to separate subcellular structures prior to TMT labeling, created a comprehensive proteomic dataset of subcellular compartments in the extracellular tachyzoite.
Cryptosporidium spp. Since standard proteomic methods demand a highly concentrated protein sample, most of the stage-specific proteomes for Cryptosporidium are lacking. While there are not as many datasets available for Cryptosporidium as the other parasites, there are a few data sets available for the mammalian pathogen C. parvum on CryptoDB  (Puiu et al. , 2004 ) from the early 2000s identifying proteins in the intact oocyst, excysted oocyst and sporozoites  (Truong and Ferrari, 2006; Snelling et al. , 2007; Sanderson et al. , 2008). These are currently the only life cycle stages where it has been feasible to collect enough material, as they are shed from large animal models, to perform proteomic analyses. Complementing the original proteomic data, there has been a quantitative study using iTRAQ with LC-MS/MS to compare sporozoites, intact oocysts, and excysted oocysts finding 302 proteins total  (Snelling et al.
Leishmania spp. As both the amastigotes and promastigotes of Leishmania can be cultured in vitro, large amounts of material can be prepared for proteomic studies. Various datasets identifying proteins in the promastigote and amastigote forms have already been created  (Brotherton et al. , 2010; Nirujogi et al. , 2014)  and are available on tritrypdb. org (Aslett et al. , 2010). Of the discussed protozoa, only in Leishmania was SWATH-MS used to identify differentially expressed proteins. Unlike the previously mentioned techniques, SWATH-MS is a data independent acquisition method. An example of this method is its use in identifying protein changes between 24 and 48 hours after L. donovani promastigote to amastigote differentiation. Routaray et al. identified 814 differentially expressed genes in the first 24 hours and 921 differentially expressed proteins at 48 hours post-differentiation  (Routaray et al.
, 2022). Another application of quantitative proteomics is thermal proteome profiling (TPP), which is an unbiased approach using TMT-labelling with mass spectrometry to identify drug targets. TPP studies generate quantitative datasets of bound proteins across a temperature gradient for all the soluble peptides in a sample. In this study Corpas-Lopez et al. used TPP to validate N-Myristoyltransferase (NMT) as a pharmacologically relevant target in Leishmania  (Corpas-Lopez et al. , 2019).
, 2012; Moon et al. , 2014; Yazdanparast et al. , 2014; Gomes-Alves et al. , 2018; Moraes et al. , 2019; Wheeler, 2020)  and applied to a range of questions, from micrograph analysis, subcellular landmark investigation and parasite motility, to insect vector behavior. Many of these parameters are common outputs from 'omics' and large screen studies. HRMAn (Host Response to Microbe Analysis) is a high-throughput, highcontent, single-cell image analysis platform which incorporates machine learning and deep convolutional neural networks  (Fisch et al. , 2019). Classification of features based on datasets has been used to distinguish phenotypic patterns of host-protein recruitment; detection and quantification of T. gondiicontaining vacuoles; and analysis of host cell responses to T. gondii infection.
More recent updates (HRMAn 2. 0) offer the possibility to investigate 3D information, and applications to other pathogens beyond T. gondii  (Fisch et al. , 2021). Despite the increased availability of image analysis tools, two 'hurdles' stand in the way of their widespread use among the parasitology community. The first is tool availability. Open source software has been pivotal for research, with  ImageJ (Schneider et al. , 2012) , CellProfiler  (Carpenter et al. , 2006) , Ilastik  (Berg et al. , 2019) , OMERO  (Allan et al. , 2012; Li et al. , 2016) , ICY  (Chaumont et al. , 2011)  being some examples of the most used tools for image analysis. A step, which remains to be more widely implemented in the parasitology imaging field is the existence of open repositories for code and image databases of both parasites and hosts, equivalent to VEuPathDB  (Amos et al.
VEuPathDB and Its Role in Parasitology A revolutionary resource in the parasitology field is VEuPathDB. This integrated database provides a repository of datasets across all "-omics" disciplines that can be accessed for free  (Warrenfeltz et al. , 2018) , ensuring equitable access to these resources. VEuPathDB is, however, not only a database, as the web-based service includes tools available for data searching, visualisation and comparisons. Moreover, the founding team is committed to democratizing science and frequently offer training world-wide to allowed hundreds of researchers across the global North and South, to access the full capacity of this tool. The online tutorials available through the resource also ensure that users with limited bioinformatics experience are able to mine the data deposited.
Most of these have already been incorporated into parasitology research, but not in high-throughput modalities. Efforts towards increasing the throughput of conventionally low-throughput techniques are shown on the left column, and include autonomous imaging (whereby user input is not required, reducing human resource demands), miniaturization and parallelization, multiplexing, and faster acquisition methods. Equally, a bottleneck for microscopy-based research is image analysis. Incorporation of artificial intelligence and machine learning, and integration with open databases and open code are promising fields for parasitology. Together, these various elements will likely play a major role on the integration of imaging into 'omics' studies to understand parasite biology. Figure created with BioRender.
ACKNOWLEDGMENTS We thank both reviewers whose comments improved our work. We thank also our various funders: RSK is funded by the  American Heart Association Postdoctoral Fellowship  (grant no. 20POST35220017 ) and  U. S. Public Health Service  (grant no. AI 139201 ). EMB is funded by the  Wellcome Trust  (grant no. 218648/Z/19/Z ). SSP is funded by  Marie Skłodowska-Curie Individual Standard European Fellowship  (grant no. 839960 ) and  Fundacão Bial  ( Project 7/2021 ). MDN is funded by  Human Frontier Science Individual Fellowship  (grant  LT000047/2019-L ).
Many of these improvements are consistent with a philosophy of open science, and have facilitated data-sharing and the creation of low-cost complex imaging equipment. Most of these have already been incorporated into parasitology research, but not in high-throughput modalities. Efforts towards increasing the throughput of conventionally low-throughput techniques are shown on the left column, and include autonomous imaging (whereby user input is not required, reducing human resource demands), miniaturization and parallelization, multiplexing, and faster acquisition methods. Equally, a bottleneck for microscopy-based research is image analysis. Incorporation of artificial intelligence and machine learning, and integration with open databases and open code are promising fields for parasitology.
The European Plate Observing System (EPOS) is a distributed research infrastructure (RI) with the mission to establish and maintain sustainable and long-term access to solid Earth science data and services by integrating the diverse national research infrastructures under a common federated framework governed by EPOS ERIC (European Research Infrastructure Consortium). This paper presents the EPOS approach to ensure financial viability and to tackle the challenge of long-term sustainability of the RI during its operational phase. The EPOS approach to sustainable operation considers the scientific impact and the promotion of scientific research as the preconditions to achieve long-term sustainability. Enabling scientific excellence implies that high-quality data and services are provided reliably and continuously to establish the RI as the enabler of investigations to solid Earth scientists.
In addition, RIs are identified as key enablers of socioeconomic impact  [OECD, 2019; van Belle et al. , 2018]. For their implementation, different organizational models exist, typically single-sited RIs, such as large research plants in one single or a few fully dedicated sites (e. g. , astronomical observatories, accelerator-based sources, nuclear reactors or extreme laser sources) or distributed RIs composed of geographically distributed observing systems and/or laboratories, remotely accessible computational resources and data repositories. RIs can offer their users physical access [see  Philippin et al. , 2019]  and/or virtual access for remote usage of scientific data and related services  [Petzold et al. , 2019]. RIs are nowadays assuming an increasingly central role in supporting open science by fostering the sharing of scientific data and products.