dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, in this case, any individual scans showing a water peak linewidth above 17. 2 Hz was discarded from the dataset (Fig. S2B ). In practice, most of the data collected after the subject nodded were discarded, which shows the efficiency of this approach to post-correct MRS data for subject motion. Moreover, while the subject was asked to return his head and neck to its initial position after nodding, this analysis shows that it was likely not the case -evidencing how very slight motion can corrupt spinal cord MR acquisition in general. Note that subject motion did not only impact the MR resonances' FWHM but also their amplitude and phase, which directly reflects a VOI displacement.
B. Normalized SNR and subject body mass index In order to explain the high discrepancy in the MRS data quality in terms of SNR and FWHM, a correlation with multiple acquisition parameters but also with anatomical and physiological parameters were attempted. In general, the SNR of localized MRS data depends on the size of the VOI and the number of averages (NA) acquired. As discussed in the main manuscript, some of the datasets, which were actually discarded because of low SNR, were acquired with a low NA compared with the rest of the group. A "normalized SNR" was calculated by taking into account the volume of the VOI and the number of averages (√NA). Interestingly, the normalized SNR depends mainly on the body mass index (BMI) of the subject and thus the voxel depth (Fig. S3 ). Current product RF coils used for spine imaging at 7T are surface coils.
D. Common downfield MRS artifact Three spinal cord MRS datasets acquired on subjects S03, S10 and S14 showed an artifact consisting of a large resonance appearing between 5 and 5. 5 ppm (Fig. S5 ). While this artifact is located in the downfield spectral range, its intensity and bandwidth can be so large that it can affect the resonances of interest in the upfield spectrum. When observed, its presence was associated with poor MR spectrum quality (low SNR and increased linewidth). The origin of this artifact remains unclear and, to the best of our knowledge, was not documented in the literature  [6]. When present in the spectrum, its intensity is stronger than any metabolite resonances and, in some cases, stronger than the residual water peak. The latter suggests that this 5/8 artifact originates from poorly saturated water protons present outside of the VOI.
E. LCModel quantification results To evaluate the robustness of the in-house MRS quantification tool developped in this study, all datasets were additionnally quantified using LCModel  [7]. Early 2021, LCModel was made free and open-source by its authors and is currently available on S. Provencher's website 1. Default parameters were set 2 and the metabolite simulated spectrum basis file was selected according to the TE of the dataset to fit. The total choline (tCho), total creatine (tCr) and total NAA (tNAA) concentrations were computed by summing the concentration estimates from GPC and PC, Cr and PCr, NAA and NAAG, respectively. Metabolites ratios relative to tCr were computed (Table  S1 ). The metabolite ratios obtained with LCModel were not significantly different (p>0. 1) from the ratios obtained with the in-house quantification software (Tables  1  and S1 ) except potentially for tNAA (p=0.
e. , they both acquire and cede land in the transnational context. In order to globally acknowledge for what was currently considered as abnormal cases, we model open access data about LSLAs extracted from the Land Matrix Initiative (LMI) open-access database into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank, to identify and rank anomalous profiles in the land trade market. We take into account three different network snapshots: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors (agriculture,mines and biofuels). Experimental results show that emerging economies (e. g. , China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one.
g. , NGOs) information sources may often be inconsistent, and in any case inaccurate with respect to the actual situation  [10]. The main source of information about LSLAs, which is also the one used in this work, is the database built a decade ago by the Land Matrix Initiative (LMI)  [11]. The LMI is a consortium of research and development partners that started collecting land-related data back in 2009, integrating information from heterogeneous sources, such as government data, public press, scientific publications and voluntary contributions from specific individuals or institutes. In database, the threshold for a land acquisition to be considered a "large scale" one is to involve more than 200 hectares of leased or sold land  [12]. While the original focus of the LMI was on agricultural international investments, the database now includes national and transnational deals concerning also many other investment sectors, such as mining and forestry.
e. , food crops (Zambia, Mozambique, Tanzania, Malawi, Georgia) and livestock (Swaziland, Nigeria). The aim of this work is to perform a deeper investigation on this phenomenon, that can be considered an anomaly in the land trade market, i. e. , less than 25% of the countries included in the transnational land trade network are characterized by this double investor/target profile. To this purpose, we will model open access data about LSLAs extracted from the Land Matrix Initiative database (https://landmatrix. org) into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank  [17] [18] [19] , to identify and rank anomalous profiles in the land trade market. While the effectiveness of complex network analysis techniques on the analysis of the land trade market has already been confirmed by our previous works on such topic  [15, 16] , the scenario we study in this work requires a methodology with peculiar characteristics.
For our experimental evaluation, we take into account three different network snapshot: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors (agriculture,mines and biofuels). Results show that LurkerRank was able to correctly identify and rank countries showing an anomalous behavior on the land trade market in different investment sectors, while simpler indicators (i. e. , the LSLA-score) are not suitable for this task. Our qualitative analysis underlines how emerging economies play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one. These emerging economies include, as expected, the BRICS countries (with China assuming a pivotal role), but also other emerging markets like Malaysia.
The Land Matrix database The networks used in this work have been modeled upon open access data extracted from the Land Matrix Initiative database. The snapshot of the database used for this work has been downloaded on March 17th, 2022, using the Deals page of the official website (https:// landmatrix. org/list/deals/). Two filters have been applied: Scope of the deal has been set to be Transnational (since for the current analysis we do not need to take into account national ones), and Implementation status to be In operation (production) (in order to take into account only the deals that are currently active, and discard the abandoned, concluded and not yet started ones). Please note that, even though the LMI database represents the most comprehensive information source about LSLAs, it is, for the reasons discussed in the introduction to this work, not exhaustive.
By consequence, some biases in the data cannot be avoided  [25]  and our results and analyses reflect current accessible data. Nevertheless, like in our previous network analysis based study on LSLAs  [16] , we make the hypothesis that such biases do not have a major impact on a global analysis like the one carried out in this work. Main characteristics of the raw data are summarized in Table  1. Based on the Intention of investment attribute of the LMI database, we extract four different networks corresponding to different investment sectors in the land trade market: • the multi-sector network, including all the transnational deals, regardless of the associated intention of investment; • the agriculture network, obtained by selecting the deals having an Intention of investment associated to the macro-domain of Agriculture on the official website, i.
Modeling the land trade network We define the land trade network as a directed graph G = (V, E, w), where V is a set of nodes, E is a set of edges, and w : E7 !R is an edge weighting function. Each node v 2 V represents a country involved in at least a transnational deal in the Land Matrix, i. e. , as target country and/ or as country of registration/origin of an investor (for this reason, in the following discussions, we may refer to a node while implicitly referring to the corresponding country). Given two nodes u, v 2 V, edge (u, v) exists in G if there is at least one deal in the LMI database such that a company based in v is involved as investor in a deal where u is the target country. Simplifying, we say that an edge (u, v) exists when v acquires land from u. Edge weights are computed as the total size (in hectares) associated to the deals involving u as target and v as location of the investor.
The LSLA-score Here, we briefly recall one of the main contributions of our previous work about Large Scale Land Acquisitions  [16] , i. e. , the LSLA-score. The aim of the LSLA-score is to provide a rank of the countries based on their investor/target role on the global land trade market. The score is proportional to the ratio between the total surface of sold and acquired land for each country, as reported in the Land Matrix database. Note that we refer to sold land with a wide meaning, which includes contracts of different nature (e. g. , leasing, renting, etc. ). The LSLA-score s for each country u in the land trade network can be formally defined as: sðuÞ ¼ log 10 1 þ P ðu;vÞ2E wðu; vÞ 1 þ P ðv;uÞ2E wðv; uÞ ! ð1Þ In Eq 1 the numerator corresponds to the total surface of land sold by u to foreign countries, while the denominator corresponds to the total surface of land that u acquires from foreign countries.
Structure of the land trade networks Table  2  reports on the structural characteristics of the four land trade networks we extracted from the LMI database: multi-sector, agriculture, mines and biofuel. All measures refer to the directed version of the graph, except for the average clustering coefficient (avg_cc) and average path length (avg_path_length) which are computed on the undirected graph. Note that the latter is not available for the agriculture network since it is not weakly connected, due to a disconnected component corresponding to the Barbados and Guyana countries (the average path length associated to the largest connected component subgraph is reported in parentheses). Note how multi-sector and agriculture share similar structural characteristics, as well as mines and biofuel.
Ranking of the anomalous land trade behaviors Table  3  shows the top countries as ranked by LurkerRank on the four land trade networks, together with their ranking score (recall that the score reported in the table is the ranking score produced by LurkerRank, and it is not related to the LSLA-score described in Sec. 2. 3). Sink (i. e. , pure investors) and source (i. e. , pure target) nodes are excluded from the ranking, in compliance with the fact that only double role countries are regarded as potential anomalies. It can be observed that multi-sector and agriculture share the same top-2 countries (Malaysia and China), even if the distance between their ranking scores is considerably higher in the second case. Note also that these two networks share 10 out of 15 top-ranked countries, even if with different positions in the ranking: this can be considered as an expected result, since investments in the agricultural sector represent the great majority of the deals reported in the Land Matrix database.
Finally, we can point out that Chinese companies also invest in large timber plantations (i. e. , the main resource for which China is a target country) in Laos and Cambodia. Focusing on Malaysia, a country that appears as the top ranked on two out of four networks, it is a massive investor, with its companies involved in more than 100 investing deals worldwide. Such deals mainly include biofuels, food crops and other agricultural investments in South-East Asia (mainly in Indonesia, but also in Laos, Cambodia and Papua New Guinea) and several African countries (Congo, Ghana, Ivory Coast, Liberia, Nigeria and Uganda) and forestry investments in Liberia and Russia. At the same time, Malaysia land is targeted by several foreign investors. The main examples is an Indian company (Avantha Group) investing in a timber plantation (again, a resource for which Malaysia invests in foreign land), that represents one of the largest deals in the entire database (more than 288k hectares).
Conclusions In this work, we focused on the phenomenon of Large-Scale Land Acquisitions (LSLAs), by performing a detailed analysis of the anomalous behaviors that can be identified in the transnational land trade market. We modeled open data about LSLAs, extracted from the Land Matrix Initiative database, in an oriented network graph, and proposed the adaptation of an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank, to identify and rank anomalous profiles in the land trade market. Analyses performed on the entirety of the available land trade deals (multi-sector network) and on specific sectors of investment (agriculture, mines and biofuels networks) showed how emerging economies (e. g. , China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one.
This implies making the compilation and the deployment of this stack reproducible, allowing one to rerun the experiment on an identical environment in one week or ten years. To reach this goal, we exploit the declarative approach for system configuration provided by the NixOS  [12]  Linux distribution. NixOS makes use of a configuration that describes the environment of the entire system, from user-space to the kernel. The distribution is itself based on the purely functional Nix package manager  [11]. The definition of packages (or system configuration in the case of NixOS) are functions without side effects, which enables Nix and NixOS to reproduce the exact same software when the same inputs are given. We extend this notion of system configuration for distributed systems in a new tool named NixOS Compose.
NixOS Compose enables to define distributed environments and to deploy them on various platforms that can either be physical (e. g. , on the Grid'5000 testbed  [1] ) or virtualized (Docker or QEMU  [2] ). NixOS Compose exposes the exact same user interface to define and deploy environments regardless of the targeted platform. We think that this functionality paired with fast rebuild time of environments improves user experience, and we hope that it will help the adoption of experimental practices that foster reproducibility. The paper is structured as follows. Section II gives the state-of-the-art on tools related to the reproducible deployment of distributed systems, and positions NixOS Compose in it. Section III presents NixOS Compose, its main concepts, the external concepts it relies on, and the users' utilization workflow for setting up a complete reproducible system and software stack.
II. STATE OF THE ART Several terminologies exist on the reproducibility of experiments. In this article we use the terms defined by Feitelson in  [15]. In particular, an experiment is said to be repeatable if one can rerun it and obtain the same results by using the original artifacts (scripts and configuration files needed to run the experiment). If the same can be achieved without using the original artifacts but by recreating them, the experiment is said to be replicable. Variation is an orthogonal concept that can be applied to repeat or replicate an experiment with a modification of some parameters. Mercier et al. emphasize in  [21]  that the production environment (the final one used to execute the experiment) is not enough to achieve variation 1 in an experiment, as the development environments contain valuable information for the experiment and are also required.
A. Reproducibility of a local software environment Several approaches exist to encapsulate a software environment. A first solution is to provide virtual machine (VM) images or containers of the environment. VMs are portable but induce a virtualization cost at runtime which hinders some experiments. Containers are much more lightweight but limit the possible usages (e. g. , Docker containers do not encapsulate the version/configuration of the Linux kernel, while this may impact how software behaves). Building VMs and containers in a reproducible fashion can be hard, but tools like Packer  [25]  can recreate identical virtualized images (either virtual machines or containers) from a unique description. Another approach is to rely on the properties of package managers. Spack  [16]  is a package manager used on HPC platforms to share software environments.
Spack does not control all the dependencies by design as it can reuse software already present and configured on the system, which is detrimental for reproducibility. Courtes et al. [10]  presents purely functional package managers (Nix  [11] , Guix  [9] ) as good candidates to share complex software environments between users. Both approaches are very similar but Nix has a more complete ecosystem as we write these lines, so we only consider Nix in the remainder of this article. Each Nix package is defined as a function without side effects, which means Nix should produce the exact same package if the function inputs are the same (source code version, build options, build and runtime dependencies and their versions, build toolchain and their versions, target architecture). A software environment is defined as a list of Nix packages.
As Nix can reproduce each package, the entire environment is reproducible. This partially solves the well known issue of Dependency Hell as Nix demands to explicit every dependency and their version to build the package. Nix is limited to userspace environments, but the environment on the kernel side matters as well. The NixOS Linux distribution, based on Nix, solves this issue by describing the entire operating system (i. e. , kernel, softwares, configuration files, services) with a descriptive Nix expression. The descriptive approach taken by NixOS greatly simplifies the work of a reproducer wanting to perform variations of the environment  [21]. Perenniality of environments can be hard to achieve on methods based on the reconstruction of a target software and every of their dependencies, as some source code can disappear over time.
B. Reproducibility of a distributed software environment Achieving reproducibility in a distributed setting brings new challenges, as the environments must be deployed on all the involved machines. A common approach is to somehow generate full system images, then to deploy them on the nodes with tools such as Kadeploy  [17]. Creating such images manually can be cumbersome. Tools such as Kameleon  [27]  take an imperative approach to build images, as they execute a pipeline of scripts to create images that contain the desired environment. Such images are not enough to achieve reproducibility, as scripts must often be executed after the image has been deployed to set up relations between nodes (e. g. , to mount a custom NFS server among the deployed nodes). These scripts are often written manually with bash or Python, and sometimes use helper libraries such as Execo  [18].
The target abstraction makes it easier to test the environment locally before deploying it at full scale. However, reproducibility of the software stack is not a focus for these solutions, and they mostly just inherit reproducibility properties of the underlying platform and technologies. Typically, one probably needs to combine EnOSlib with tools such as Kameleon to make sure the kernel is reproducible, and the end user would be responsible for the reproducibility of the provisioning scripts. Projects such as NixOps  [22] , deploy-rs [28] or Disnix(OS)  [3, 4]  enable to activate new NixOS configuration on target machines, but they are limited to machines that already run NixOS. These technologies only change the running configuration on the machines without rebooting them, which keeps a state on the machines and is detrimental for reproducibility.
A Nix system profile defines the configuration of the system (packages, initrd, etc. ). Among many features, a profile can define filesystems such as NFS and mount them automatically at boot time. Figure  3  depicts an example of user profile containing the Batsim application  [14] , which requires the SimGrid  [6]   Traditional package managers fetch a built version of the package from a mirror, but information on how they have been built is unknown. In the case of Nix, the package is described as a Nix function that takes as input the source code and how to build it. If the package with these inputs has already been built and is available in the Nix caches (equivalents of mirrors) it is simply downloaded to the Nix Store. Otherwise, it is built locally and added to the Nix Store. /home/alice/.
2) Composition: A composition is a Nix expression describing the NixOS configuration of every role in the en-vironment. Listing 1 shows an example of composition for k3s. Please note that the composition in Listing 1 contains the nodes keyword on line 4 to define the various roles (instead of the roles keyword). This is done on purpose to make NixOS Compose's syntax retro-compatible with the syntax used in NixOS tests 1  (these tests are part of NixOS and enable to start virtual machines with QEMU with the defined environment, and execute a Python script to test the application inside the environment). The composition in Listing 1 defines the open port of the server (line 10-12), the available packages (lines 7-9 and 23-25, packages are k3s and gzip), as well as the systemd services (lines  13-19 and 26-31).
Connect to the nodes: nxc connect [node name]. This opens a connection to the desired node. If name is omitted, a terminal multiplexer 3 opens with one pane per node, which is convenient to run commands on the nodes. 3 tmux: https://github. com/tmux/tmux 2) Distributed Deployment: Once the environment has been tested with local flavours, it can be tested in a distributed system. 1. Building the image: nxc build -f g5k-ramdisk or nxc build -f g5k-image 2. Reservation of the nodes to use for the deployment: depends on your platform resource manager. For example salloc for Slurm  [32]  or oarsub for OAR  [5]. 3. Deploy the environment: nxc start. 4. Connect to the nodes: nxc connect [node name]. Figure  4  summarizes the NixOS Compose workflow. NixOS Compose aims at making the transition between platforms as seamless as possible.