dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank  Emmanuel Lemichez  ( Université Côte d'Azur, INSERM ,  U1065 ,  C3M ,  Nice, France)  and  Annie Fafin  ( Secrétariat Pôle Hépatite, Hôpital L'Archet 2, Nice, France ) for their help with the study's logistics. The Cytokine Ascites group collaborators:  Annie Raoult ,  Florence Tanne ,  Christopher Payan ,  Carole Triboulot ,  Sylvia Mari ,  Patrick Hastier ,  Cristel Fissore-Magdelein ,  Marie-Line Peritore ,  Véronique Blanc-Amrane ,  Eugenia Marine-Barjoan ,  Jenny Vibert.
Our work, part of a broader project entitled "Large scale assessment of land degradation to guide future investments in sustainable land management in the Great Green Wall countries (Global Environment Facility grant 9825), aims to fill those gaps. In addition, it maps the links between the roles of the different actors (donors, fund mobilizers, operators, and implementers). To this end, we have developed a spatially referenced data table that lists and maps temporally sequenced (2008-2021) geolocated data. These data have been collected from publicly accessible sources.
Data Collection on SLM Actions and Construction of a Spatially Referenced Data Table, Viewable in GIS, for the Traceability of Degraded Land Reclamation Actions (DLRA) Seven main steps were followed (Figure  1 ). Step 1: Information mobilization The aim was to collect and centralize all types of public data dealing with SLM actions carried out in Niger between February and June 2022, from the oldest to the most recent, in various formats and media, available online or distributed among the structures holding the data (public, parapublic, or private). To identify these different structures, a pre-established survey form was distributed to an initial list of 20 active structures, made up of NGOs, projects, and development programmes present in Niger. Interviews were also conducted with civil society and bilateral cooperation actors in order to make a brief diagnosis of the databases of SLM actions in Niger.
The consultation of technical documents (such as study reports), scientific articles, websites, and institutional databases dealing with degradation, treatment, or SLM, helped complete the information thus collected. Step 1: Information mobilization The aim was to collect and centralize all types of public data dealing with SLM actions carried out in Niger between February and June 2022, from the oldest to the most recent, in various formats and media, available online or distributed among the structures holding the data (public, parapublic, or private). To identify these different structures, a pre-established survey form was distributed to an initial list of 20 active structures, made up of NGOs, projects, and development programmes present in Niger. Interviews were also conducted with civil society and bilateral cooperation actors in order to make a brief diagnosis of the databases of SLM actions in Niger.
The consultation of technical documents (such as study reports), scientific articles, websites, and institutional databases dealing with degradation, treatment, or SLM, helped complete the information thus collected. Step 2: Extraction of relevant data according to traceability criteria The information collected was sorted, and the useful data were extracted and integrated into a single data table in Excel format. From the range of SLM actions or those designed to combat land degradation (ADA), the DLRA implemented in the field were selected (Figure  2 ). Only geolocated DLRAs were selected for our work. Geolocation is a key traceability criterion, as it allows anyone to return to the places where the DLRAs were implemented and to avoid having to inventory the same action several times. The other traceability criteria are the type of DLRA; the area treated, the spatial entity (region, department, commune, village), the year, the purpose; and the donors, the actors who mobilize these donors, the operators, and the implementing entities on the ground.
Step 3: Co-construction of the data table The result of this work of collecting and organizing data on DLRAs was presented to some thirty actors representing the various structures in order to gather their analysis on Step 2: Extraction of relevant data according to traceability criteria The information collected was sorted, and the useful data were extracted and integrated into a single data table in Excel format. From the range of SLM actions or those designed to combat land degradation (ADA), the DLRA implemented in the field were selected (Figure  2 ). the construction of this data table and to enrich it with projects that had been overlooked until then. Step 4: Classification of data according to a harmonized nomenclature Each DLRA was described according to the official national technical sheets for SLM actions in Niger  [24, 25].
When the DLRA at a site is a combination of several actions, the name of the action with the largest treated area is used. To define the roles and responsibilities of the different actors, it was necessary to return to the project documents. Four categories of actors were identified along the chain (Figure  2 ): (1) donors (Do); (2) fund mobilizers (Fm)-who identify and mobilize funds; (3) operators (Op)-who put DLRAs in action; and (4) and implementers (Im)-who implement DRFLAs in the field. Step 5: Consolidation of geolocated data The collection of data from multiple sources generated geolocation errors and inconsistencies. Systematic georeferencing and localization work was then carried out: (1) by assigning a georeference to the names of non-georeferenced localities, and conversely, (2) by assigning the name of a locality to the georeference without locality.
All these opera- Only geolocated DLRAs were selected for our work. Geolocation is a key traceability criterion, as it allows anyone to return to the places where the DLRAs were implemented and to avoid having to inventory the same action several times. The other traceability criteria are the type of DLRA; the area treated, the spatial entity (region, department, commune, village), the year, the purpose; and the donors, the actors who mobilize these donors, the operators, and the implementing entities on the ground. Step 3: Co-construction of the data table The result of this work of collecting and organizing data on DLRAs was presented to some thirty actors representing the various structures in order to gather their analysis on the construction of this data table and to enrich it with projects that had been overlooked until then.
Step 4: Classification of data according to a harmonized nomenclature Each DLRA was described according to the official national technical sheets for SLM actions in Niger  [24, 25]. When the DLRA at a site is a combination of several actions, the name of the action with the largest treated area is used. To define the roles and responsibilities of the different actors, it was necessary to return to the project documents. Four categories of actors were identified along the chain (Figure  2 ): (1) donors (Do); (2) fund mobilizers (Fm)-who identify and mobilize funds; (3) operators (Op)-who put DLRAs in action; and (4) and implementers (Im)-who implement DRFLAs in the field. Step 5: Consolidation of geolocated data The collection of data from multiple sources generated geolocation errors and inconsistencies.
Systematic georeferencing and localization work was then carried out: (1) by assigning a georeference to the names of non-georeferenced localities, and conversely, (2) by assigning the name of a locality to the georeference without locality. All these operations were carried out using tools such as Google Earth and Geonames (https://www. geonames. org/, accessed on 12 December 2022). Only those DLRAs that were consolidated in this way from a geolocation point of view were retained. The subsequent cleaning of the data table consisted of (1) deleting the data collected that did not relate to the DLRA; (2) deleting the DLRAs that did not contain all the descriptive data that would allow them to be traced (cf. step 2); (3) correcting the input errors detected and standardizing the formats of the geographical coordinates; and (4) deleting the duplicates.
Step 6: Passage to a geolocated data table that can be viewed in GIS Once the geolocated dataset and other traceability criteria had been filled in, their Excel table format (*. xls) was converted into text format (*. txt) and then into GIS format, using the mapping software Qgis. Step 7: Verification of the adequacy of the geographical coordinates with respect to the administrative boundaries of Niger Viewing the DLRAs in Qgis revealed that some of them are outside Niger or do not fit into the internal administrative division (region, department, commune). Using the attribute table and tools such as Google Earth and Geonames, they were then brought back to the corresponding administrative boundaries.
Data Analysis The analytical work is based on a single parameter: the total area of land treated contained in our date base (e. g. , 279,074 ha). The analyses (in Excel 2013) were carried out on the harmonized and consolidated DLRA data. The e!Sankey software (version 5. 2. 1) was used to visualize (i) for each of the roles (e. g. , Do, Fm, Op, and Im), the respective contribution to each of the categories of actors (see Tables  S1-S5 ); and (ii) their interactions (e. g. , the funds of which donors are mobilized by which fund mobilizers, and then used to support the actions of which operators, who in turn entrust which category of implementers to carry out the actions required to treat a total of 279,074 ha). The size of the nodes (Do, Fm, Op, and Im) and the links between each category of actors depend on their contribution to the role and the interactions of the actors.
Data Table: Keys to Identifying and Harmonizing the Nomenclature of DLRA in Niger Given the diversity of DLRAs in terms of an official nomenclature (see Step 4), they have been grouped together according to two levels of structuring (Figure  3 : in green, first level; in grey, second level). This grouping is the result of a consensus between Nigerien LCD experts at the end of a three-day workshop. tained in our date base (e. g. , 279,074 ha). The analyses (in Excel 2013) were carried out on the harmonized and consolidated DLRA data. The e!Sankey software (version 5. 2. 1) was used to visualize (i) for each of the roles (e. g. , Do, Fm, Op, and Im), the respective contribution to each of the categories of actors (see Tables  S1-S5 ); and (ii) their interactions (e. g. , the funds of which donors are mobilized by which fund mobilizers, and then used to support the actions of which operators, who in turn entrust which category of implementers to carry out the actions required to treat a total of 279,074 ha).
Dugouts at the Heart of Degraded Land Treatment in Niger The DLRA data table shows that a total of 279,074 ha of degraded land treated in Niger has been georeferenced over the period 2008 to 2021. In general, dug-out structures remain at 80% (i. e. , 223,822 ha of the total area of treated land) at the heart of the treatment of degraded land in Niger. However, the georeferenced treated areas vary significantly between the different types of DLRA (Figure  4 ). Thus, with 170,424 ha (61%), half-moons occupy the largest area of total land treated. They are followed by dune fixation and benches with, respectively, 39,230 ha (14%) and 38,072 ha (13%). The other DLRA represent only 12% of the total area treated, with 7806 ha (3%) for stone cordons, 7394 ha for forage seeding (3%), 4534 ha (2%) for trenches, 3122 ha (1%) for plantations, 3090 ha (1%) for ANR, 2415 ha (0.
Mapping the Actors' Role: The State of Niger, a Key Player in the Fight against Land Degradation in Niger The contributions of the more than 100 stakeholders identified in our database to the 279,074 ha processed by DLRAs can be distinguished according to their "functional" role: (1) as donors (Do), (2) as fund mobilizers (Fm), (3) as operators (Op), and (4) as implementers (Figure  9 ). Ten categories of donors financed the implementation of DLRAs in Niger (Table  S1 ) and for various areas (Figure  9 ). The UN agencies and Bretton Wood institutions are the main donors, whose financing represents, respectively, 36% (100,565 ha) and 34% (96,825 ha) of the total area of DLRAs. The Government of Niger, with its own funds, comes in third place with 12% (35,716 ha) of DLRAs. Other donors (e. g.
For example, the stone cordons require the presence of stone, a resource which the regions located on the plateaus and plinths are endowed with, as is the case in Tahoua, Agadez, Dosso, and Tillabéri. The micro-basins are constructed in rainy areas with relief (Tillabéri) or soils sensitive to linear erosion that can create koris and gullies (Maradi). The zaï are more suitable for DLRAs on the crested soils of agricultural areas (Tahoua) or to optimize organic fertilization on degraded sandy soils (Maradi). The contributions of the more than 100 stakeholders identified in our database to the 279,074 ha processed by DLRAs can be distinguished according to their "functional" role: (1) as donors (Do), (  2 ) as fund mobilizers (Fm), (3) as operators (Op), and (4) as implementers (Figure  9 ).
g. , the Climate-Sensitive Agriculture Support Project, the Resilience Building Project to Combat Food Insecurity in Niger, and the SLM Project). International development programmes and projects (e. g. , World Food Programme and Action Against Hunger) contributed 19% and 11% of the DLRAs, respectively. In our database, national development programmes and sub-regional projects have very little involvement in SLM actions. At the very end of the chain of actors (Figure  9 ), national development projects (e. g. , the Community Action Project for Climate Resilience and the Emergency Project in Support of Food Security and Rural Development) were the main contributors to the implementation of the DLRAs (31%) on the ground (Table  S6 ). In contrast, sub-regional development projects, international development cooperation agencies, and local authorities contributed very little to field interventions (merely 1%).
Development programmes and projects have been the most effective vehicles to put DLRAs into action (Table  S4  and Figure  9 ). The largest areas of DLRA, amounting to 149,137 ha (53%), were implemented under national development projects (e. g. , the Climate-Sensitive Agriculture Support Project, the Resilience Building Project to Combat Food Insecurity in Niger, and the SLM Project). International development programmes and projects (e. g. , World Food Programme and Action Against Hunger) contributed 19% and 11% of the DLRAs, respectively. In our database, national development programmes and sub-regional projects have very little involvement in SLM actions. At the very end of the chain of actors (Figure  9 ), national development projects (e. g. , the Community Action Project for Climate Resilience and the Emergency Project in Support of Food Security and Rural Development) were the main contributors to the implementation of the DLRAs (31%) on the ground (Table  S6 ).
Traceability and Sharing of DLRA Data in Niger An effective SLM policy requires a comprehensive and spatially based assessment of the current situation  [26]. Our study revealed the existence of a few non-operational sectoral DLRA databases  [20, 27, 28]. Most of the available data on DLRA suffer from a lack of accessibility (most often not organized in a table or database), reliability, and geolocation. However, the vast majority of data are used by some actors to construct indicator tables, graphs, or maps  [28, 29]. However, this use is limited to each of the actors, without sharing or archiving according to common criteria  [20, 28, 30, 31]. Although useful for each of the actors, this fragmentation of knowledge is not up to the challenges, on a national scale, of monitoring and evaluating actions and their impacts, avoiding the repetition of what has already been done, and the necessary coordination of the various actors in their respective roles.
Their respective actions could be based on the same body of data  [25]. In this perspective, the need to access spatially referenced databases with a common architecture has been demonstrated  [25]  according to a harmonized nomenclature. The choice to build a single data table in this work is based on the absence of such a database. The collection of available data, which are scattered, of heterogeneous quality, and of very different natures and formats  [41, 42] , and then formatting and harmonizing them, was therefore a prerequisite for their processing  [43, 44]. Based on criteria useful for their traceability, it was possible to set up a national data table integrating both information on geolocated DLRAs and information on the network of actors involved. The national data table, which is probably incomplete, nevertheless has the potential to be a useful source of information for the formulation of recommendations to decision makers in the implementation of strategies for capitalizing on and scaling up DLRA.
The method of construction as well as the final structure of this Niger data table could constitute a replicable model in other countries of the GGW zone. In the future, it can help the various data providers to adapt their own data tables (structure and nomenclature) to feed (automatically or not) the national data table more easily. However, ways must be found to maintain and feed such a spatially referenced data table at the national level in the long term for the spatiotemporal monitoring and assessment  [25, 28, 29, 45]  of DLRA. One way to do this would be to strengthen the skills of the structures that produce these data so that they can maintain such a database, and then build a central national web portal (or even a regional one in the context of initiatives such as the GGW). Such a portal would make it possible to query the data tables distributed in the various structures in order to generate the national table automatically (in the sense of using the most up-to-date data).
This web portal could also make it possible to visualize the data on maps according to a standardized graphic semiology, and to navigate through the data according to a geographical query. These perspectives raise a number of questions: What is data governance? Which structures should produce the data along the chain of actors, from donors to the entity that executes DLRA on the ground? Where to host such a centralized service? What steps should be taken to ensure that the distributed data tables are compatible with the query on the central service? How can the membership of data-producing structures be increased? etc. If each GGW country were to organize such spatially referenced DLRA data systems, this could help, for example, to feed the regional SIOBAP system (System of Information, Observation, Early Warning and Response) that the Pan-African GGW Agency (PAGGW) is setting up.
Conclusions Reducing and slowing down land degradation, and rehabilitating or restoring degraded land are key levers in achieving sustainable development for the benefit of populations-particularly those whose livelihood relies on ecosystem services. Combatting land desertification has been declared a national priority in the Niger, echoing target 15. 3 of the SDGs. Based on a collection of public data held by different entities, we have built a unique database consisting only of georeferenced data, containing all the information not only on DLRA, but also on the actors and their roles as donors, fund mobilizers, operators, and implementers. This is a clear choice based on the objective of our work. We are aware that not all field actions are itemized. However, our work demonstrates the added value of creating such a georeferenced database management system in order to (i) deploy targeted sustainable land management initiatives that complement both past and ongoing actions (thus avoiding multiple actions in the same place) and (ii) synergize all the stakeholders.
Nevertheless, the task of identifying, harmonizing/standardizing, and geolocalizing DL-RAs on a country-wide scale is considerable. The work in the Niger is unfinished but offers a roadmap towards consolidating these achievements and ensuring their transferability to other countries. The creation and continuous feeding of a database management system must also be undertaken. To this end, raising awareness and mobilizing all stakeholders to contribute collaboratively to such a common dataset represents a major challenge. However, the efforts involved in such an undertaking are small compared to the benefits of acquiring such a database. It will help in assessing and monitoring the contribution of DLRAs to the SDGs, and thus give value and visibility to the role of each stakeholder. The establishment of scientific observatories anchored in the territories can meet this ambition.
DLRAs Are Unevenly Distributed in Niger 3. 3. 1. Analysis by Administrative Region of the Spatial Distribution of DLRAs From 2008 to 2021, the proportions of land treated in each year relative to the total over the period vary from year to year (Figure 6). In 2012, the most land was treated by DLRA. Indeed, almost 20% of all land treated was treated in 2012, that is 50,844 ha. In 2013, 2014, 2015, 2016, 2017, and 2021, about 10% of the total 279,074 ha recorded in our database was treated per year. The years 2008, 2009, 2010, 2011, 2018, 2019, and 2021 saw low investments in SLM. The political will of the State of Niger is illustrated through the launch of the 3N programme "Nigériens Nourrissent les Nigériens", and the implementation of various programmes (e. g. , COMPACT of the Millennium Challenge Account, the Programme for the Development of Family Farming in the Regions of Maradi, Tahoua and Zinder (ProDAF), and the Support Project for Rural Activities and Financing of Agricultural Commodity Chains in the regions of Agadez and Tahoua).
3. 3. 3 DLRAs Are Unevenly Distributed in Niger 3. 3. 1. Analysis by Administrative Region of the Spatial Distribution of DLRAs From 2008 to 2021, the proportions of land treated in each year relative to the total over the period vary from year to year (Figure 6). In 2012, the most land was treated by DLRA. Indeed, almost 20% of all land treated was treated in 2012, that is 50,844 ha. In 2013, 2014, 2015, 2016, 2017, and 2021, about 10% of the total 279,074 ha recorded in our database was treated per year. The years 2008, 2009, 2010, 2011, 2018, 2019, and 2021 saw low investments in SLM. The political will of the State of Niger is illustrated through the launch of the 3N programme "Nigériens Nourrissent les Nigériens", and the implementation of various programmes (e. g. , COMPACT of the Millennium Challenge Account, the Programme for the Development of Family Farming in the Regions of Maradi, Tahoua and Zinder (ProDAF), and the Support Project for Rural Activities and Financing of Agricultural Commodity Chains in the regions of Agadez and Tahoua).
Background Osteoarthritis is an age-related disease that currently faces a lack of symptomatic treatment. Inflammation, which is mainly sustained by pro-inflammatory cytokines such as IL-1b, TNF, and IL-6, plays an important role in osteoarthritis progression. In this context, pro-inflammatory cytokines are widely used to mimic the inflammatory component of osteoarthritis in vitro. However, the therapeutic failures of clinical trials evaluating anti-cytokines drugs highlight the lack of overall understanding of the effects of these cytokines on chondrocytes. Methods Here, we generated a comprehensive transcriptomic and proteomic dataset of osteoarthritic chondrocytes treated with these cytokines to describe their pro-inflammatory signature and compare it to the transcriptome of non-osteoarthritic chondrocytes.