dataesr/scientific-paragraphs-categorization huggingface dataset:
The dominating importance of the satellite drought indices in March for the model based on XGBoost supports this assumption (Figure  4 ). 2 ). Other striking comments can be made by analyzing the importance of the three datasets (Figure  4 ): (1) the weather data dominates largely in January and, to a lesser extent, in February, while a strong shift is observed in March when satellite drought indices take the lead over the two other datasets. This is in agreement with the already observed high correlation between yields and precipitation around emergence in October and November and between yields and temperature in December during the tillering stage  [11] ; (2) Likewise, the importance of climate indices decreases with the lead time and their contribution is the lowest of the three datasets apart from in January when it contributes to 20% like the satellite drought indices.
Nev theless, a significant contribution is observed in February (35%) and in January (20%). Th is because VCI and TCI were found to be significantly correlated to final yields in Janua and February and because SMCI is significantly related to yields as early as Octob around the emergence stage  [10]. Indeed, the high moisture in the upper soil layers at th time facilitates the emergence and significant rainfall event during October-Decemb promotes the farmer to seed, leading to an increase in cereal production  [10, 12]. 2. In order to assess the practical performance of the developed models to predict yie in Morocco, a "leave-one-year-out" experiment, mimicking the practical forecasting co ditions of a manager who wants to predict yields for the season to come based on t historical dataset, is tested.
75 for MLR while the correlations for the non-linear methods are above 0. 90). While a slight improvement of the model metrics is observed when going from January to February, considering predictors in March leads to a significant jump in the metrics with an RMSE close to the international standard of 0. 20 t. ha -1 for the XGBoost method and, to a lesser extent, for the RF model. This is probably related to the very high correlation between NDVI around the crop development peak in March and wheat yields that were already shown by various authors  [11, 87]  giving a large weight to VCI at this time. The dominating importance of the satellite drought indices in March for the model based on XGBoost supports this assumption (Figure  4 ). Other striking comments can be made by analyzing the importance of the three datasets (Figure  4 ): (1) the weather data dominates largely in January and, to a lesser extent, in February, while a strong shift is observed in March when satellite drought indices take the lead over the two other datasets.
This is in agreement with the already observed high correlation between yields and precipitation around emergence in October and November and between yields and temperature in December during the tillering stage  [11] ; (2) Likewise, the importance of climate indices decreases with the lead time and their contribution is the lowest of the three datasets apart from in January when it contributes to 20% like the satellite drought indices. Indeed, the highest correlation with yields was found in December and January for NAO and SCA, respectively, while the correlations with the SST leading modes peak in October and February for Atlantic Niño and Atlantic Tripole modes, respectively. In addition, linkages between climate indices are, in particular, based on SST, and yields occur through teleconnection, meaning that the relationships are very indirect.
In order to assess the practical performance of the developed models to predict yield in Morocco, a "leave-one-year-out" experiment, mimicking the practical forecasting conditions of a manager who wants to predict yields for the season to come based on the historical dataset, is tested. Figure  5  presents the average of the observed and the predicted yields using the three non-linear methods (MLR was excluded with regards to its poorest performance) for a lead time from 4 to 2 months before harvest. As already highlighted, the statistical metrics improve when going from January to March but the models predict yields with reasonable accuracy as early as January. Beyond the average statistical metrics, the ability of the forecasting models to predict extreme values is another important feature of seasonal prediction.
Discussion The proposed approach is based on a combination of three different datasets to for cast grain yields. From these datasets, the predictors were carefully selected based on st tistically significant correlations with grain yields and biophysically sound mechanism as explained in previous studies  [10] [11] [12]. Atmospheric and oceanic indices are taken proxies of local temperature and rainfall conditions. Interestingly enough, the impact oceanic circulation on local weather can be not concomitant because of the remote natu of these phenomena occurring though teleconnections processes, such as for the Atlan "El Niño" mode of SST variability. This explains why climate indices are important pr dictors for very early forecasting in January. Some authors have even based their mod ing experiment on large scale climate information only to forecast yield, including Le mann et al.
Discussion The proposed approach is based on a combination of three different datasets to forecast grain yields. From these datasets, the predictors were carefully selected based on statistically significant correlations with grain yields and biophysically sound mechanisms as explained in previous studies  [10] [11] [12]. Atmospheric and oceanic indices are taken as proxies of local temperature and rainfall conditions. Interestingly enough, the impact of oceanic circulation on local weather can be not concomitant because of the remote nature of these phenomena occurring though teleconnections processes, such as for the Atlantic "El Niño" mode of SST variability. This explains why climate indices are important predictors for very early forecasting in January. Some authors have even based their modeling experiment on large scale climate information only to forecast yield, including Lehmann et al.
Conclusions Crop yield forecasting provides critical and timely information to enable farmers to make quick decisions to increase yields through improving agricultural practices during the growing season. In addition, it allows the modeling of global and local market prices  [98]. The main objective of our study was to develop an approach to forecasting cereal yield in Morocco based on multi-source data and machine learning techniques. To this objective, this study presents a methodology based on different machine learning approaches (MLR, SVM, RF and XGBoost) to predict the cereal yield over Morocco for several lead times prior to harvest using freely available datasets including satellite-based drought indices, weather data and climate indices. Our results show that combining satellite-based drought indices, weather and climate data as predictors of cereal yield provided a better forecasting accuracy than using any single data source.
In line with our results, several studies pointed out that the use of multi-source data increases the accuracy of the machine learning model for yield prediction  [52, 54, 56]. XGBoost outperformed the other machine learning techniques by explaining 93% of yield variation at the scale of the country (RMSE = 0. 23 t. ha -1 ). Interestingly enough, the value of RMSE is close to the acceptance threshold of 0. 2 t. ha -1 used in European agro-statistics  [86]. It is also shown that the prediction accuracy increases as more observations along the growing season are added for all machine learning algorithms. Finally, the development of models at the regional level for each group of provinces improved the skills of yield prediction with regards to one "global" model applied to all provinces by decreasing the RMSE by about 4% to 36% depending on the province and the time of prediction, which is due to the high variability of cereal yield from one province to another.
Funding: This work was carried out within the framework of the  Joint International Laboratory TREMA  (http://lmi-trema. ma,accessed on  31 July 2021 ). This work was funded by the  ERANETMED03-62   CHAAMS  project, the  ACCWA  project, grant agreement no:  823965  and by  SAGESSE PPR/2015/48. E. Bouras  was supported by a fellowship from the  ARTS program  from  IRD, France. The  H2020   PRIMA ALTOS  project, MISTRALS/SICMED2, PHC Toubkal # 39064WG/2018  and  PRIMA-IDEWA  project are also acknowledged for additional funding.
All statistical analyses were performed with R Studio 3. 5. 2 software. All women received written information about the study and agreed to participate. The Institutional Review Board, Comit e de protection des personnes-IV Sud-Est, approved the research protocol (n 18/002). This study was performed with involvement of women with endometriosis who contributed to the development of the ENDOPAIN-4D questionnaire  (16). Two members of the French Association of Patients with Endometriosis (Endo-France) were invited to comment on the study design and participated in the interpretation of the results and the writing of the manuscript.
Acknowledgments: The authors thank the ''Direction a la  Recherche Clinique et a l'Innovation'' of Versailles (France) and the ''Centre Hospitalier Interr egional'' de Poissy (France)  for sponsoring the study and for their help in data collection. The study was funded by the 'Direction a la  Recherche Clinique et a l'Innovation' of Versailles, France and the 'Institut de Recherche en Sant e de la Femme' (IRSF). They had no role in the analysis and interpretation of the data. DIALOG: You can discuss this article with its authors and other readers at https://www. fertstertdialog. com/posts/32862.
2018) , survivor narratives (e. g. , Murphy 2014), and interviews with company executives (e. g. ,  Crane et al. 2019). The hidden nature of the phenomenon  (Konrad et al. 2017) , the absence of targeted datasets  (Weitzer 2015) , and the difficulty of gaining access to primary data sources  (Chesney et al. 2019)  prevent researchers from conducting quantitative empirical studies. Although sparse, quantitative research on labor exploitation tends to focus on a country-level estimation of forced labor victims (Global Slavery Index (GSI) 2018), or macro-level factors causing the prevalence of forced labor and slavery in broader terms  (Landman and Silverman 2019). While these studies offer important insights into the vulnerability factors that lead to labor exploitation, they do not provide decision support tools that humanitarian organizations and governments can use to fight this phenomenon on the ground  (Konrad et al.
Satellite Data and Humanitarian Operations In this study, we use satellite remote sensing techniques for locating informal worker settlements. These techniques have been widely used in various disciplines, ranging from natural hazards (e. g. ,  Kougkoulos et al. 2018 , Sokat et al. 2018)  to crop science (e. g. ,  Daughtry et al. 2005) , and urban planning (e. g. ,  Pham et al. 2011) , among others. Satellite technologies have also been used for various applications in the humanitarian sector, such as remote sensing (i. e. , imagery and mapping), telecommunication, GPS positioning, vehicle tracking, or predicting extreme weather events  (Delmonteil and Rancourt 2017, Starr and Van Wassenhove 2014). In this review, we focus on remote sensing, the technology we use in our study. Remote sensing applications have been used to assess damage after disasters (e.
There is a clear need for operational decision-making support so that these organizations can better plan their interventions for improving workers' living conditions. These interventions include fire safety controls, the provision of sanitary supplies, food, water, and clothing, as well as legal support services and Greek language lessons. Also, timely interventions are becoming increasingly important in Greece; large migrant flows cause congestion in informal settlements, which in turn can lead to significant fire risks. Our empirical case in Nea Manolada provides two main insights, which we describe in detail in Section 4: (1) a robust estimation method of informal settlement locations in the area, based on the use of remote sensing data; and (2) an MCDA for informal settlement prioritization, providing a complete ranking of a subset of informal settlements, from the highest to the lowest risk of labor exploitation.
Data and Methods The data collection process took place between April 2018 and November 2019. It incorporated data from satellite imagery, face-to-face administered questionnaires, and personal observations. Figure  1  summarizes the five main steps of the multi-method approach we developed to assess the labor exploitation risks of informal settlements. This process, which can be updated weekly, offers governments and humanitarian organizations a cost-effective way to constantly monitor the area and identify the creation of new informal settlements. The first step consists of remote sensing the area using Pléiades satellite imagery (AIRBUS 2020) to identify all potential informal settlements that will then be verified on the ground. Once the total number of informal settlements is estimated, we use another source of satellite imagery, PlanetScope (Planet Labs 2020), which has lower spatial resolution (3 m instead of 50 cm) but higher temporal resolution (every week instead of every six months), in order to estimate potential locations for new settlements.
Ground truthing in Step 2 aims to verify the existence of the potential locations on the ground. The combination of remote sensing and ground truthing is very costeffective, because only suspected locations are investigated, as opposed to covering 100% of the area, which would be nearly impossible. In Step 3, the inspection teams collect data from each settlement using a simple questionnaire to address all criteria for the MCDA (see Table  1 ). Steps 2 and 3 could be combined, but they require teams with different skillsets, so we present them as separate activities. To test our approach, we performed the ground truthing step on all identified settlements but conducted the field data collection in only six randomly selected settlements (due to time constraints). In Step 4, the collected scores on each criterion for each settlement were inserted in the MCDA software Visual PROMETHEE, producing the full risk ranking of the informal settlements.
We then used this satellite imagery to manually investigate the entire strawberry production area (~140 km 2 ) for more informal settlements. While the Pléiades imagery data offer enough resolution to allow such manual identification of settlements, it is a difficult and time-intensive process. In addition, as the Pléiades imagery data are mostly provided semiannually, unless requested commercially, it is impossible to use them for a continuous monitoring system that is updated on a frequent (weekly) basis to inform the MCDA. For that reason, it is imperative to use data that are updated more frequently to allow for the dynamic monitoring of the area (i. e. , to identify potential locations of new settlements). 4. 1. 2. Informal Settlement Monitoring using PlanetScope Data. In order to monitor informal settlements in the area, we used PlanetScope imagery data, which are updated weekly, but offer a lower spatial resolution-3 m instead of 50 cm for the Pléiades imagery (see Appendix S5 for a visual comparison of these two datasets).
Field Data Collection After confirming the presence of informal settlements through ground truthing, we set out to collect data in each settlement to inform the MCDA method (see Section 4. 4 for the specific data collected). We conducted this step in June 2019, just after the first ground truthing visit, when between 20 and 40 migrant workers were living in each settlement. These data were collected by two authors (both fluent Greek and English speakers) through face-to-face administered questionnaires in each settlement (see questionnaire in Appendix S7). Upon arrival in each settlement, the authors briefly explained the objective of the study, and asked the three most knowledgeable respondents to answer their questions. The authors guaranteed the anonymity of the workers' responses to ensure they received frank and unbiased answers and to avoid issues with the farmers.
While administering the questionnaires, the authors assessed the hygiene and safety conditions of each informal settlement, which enabled data triangulation between the migrant workers' responses and the researchers' observations. The authors also assessed one criterion that was not included in the questionnaire-the visibility of the settlement from the nearest road or village (g 8 ). We conducted this field data collection in six randomly selected informal settlements. Due to time and resource constraints, we had to limit our data collection to these six settlements to demonstrate the applicability of our approach. However, our proposed approach requires analyzing all identified settlements in an area. While this is certainly a time-consuming *: (+) the higher the value of the evaluation the higher the risk, (-) the lower the value of the evaluation the higher the risk.
Selection and Basic Operating Elements of MCDA Method and Software. Transparency in the choice of methodology and software is an essential part of an accurate decision-making process  (Wene and Espejo 1999). Due to the plethora of existing MCDA methods and software, new attempts have been made to group multiple methodologies under a single software with more user-friendly interfaces (e. g. , Decerns software by  Yatsalo et al. 2016 ). Nevertheless, not all methods and software are easily accessible and sometimes come at a cost. Wa ˛tróbski et al. (  2019 ) underlined that method selection has been an issue in the field of MCDA and therefore created a publicly available selection system. We followed this selection system (see Appendix S8 for details), which showed that the preferred ranking organization method for enrichment evaluation (PROMETHEEsee  Brans and Vincke 1985, Brans et al.
1986 ) is the most suitable method for our work. PROMETHEE is ideal for ranking problems and works with both equal and different weights for each criterion. PROMETHEE has been extensively applied in the literature, apparently due to the variety of user-friendly software supporting this method, such as PROMCALC  (Brans and Mareschal 1994) , Decision Lab 2000, Visual PRO-METHEE, or D-SIGHT  (Hayez et al. 2012) , as well as to its strength in outranking of alternatives  (Thies et al. 2019). In this study, we use the Visual PROMETHEE software, which is available for free for academic institutions. Appendix S9 provides more details about the PROMETHEE ranking system we used.
Remote Sensing for Identification and Monitoring of Informal Settlements The analysis conducted on the Pléiades satellite imagery (April-May 2018) indicated 52 informal settlements, composed of 2-20 housing units per informal settlement (about 400 housing units in total). The ground truthing we conducted in June 2019 confirmed the presence of all informal settlements identified through satellite observations (except four identified as depots for storing agricultural material). After March 2019, we used PlanetScope data in combination with ground truthing to monitor the evolution of informal settlements in the area during the 2019 harvesting season. Two additional informal settlements were identified from March to June 2019 (and confirmed through a ground truthing visit in October 2019), leading to a final count of 50 confirmed informal settlements.
Settlements A 4 and A 5 have similar characteristics. Even though the hygiene and safety conditions are average, these sites are hidden and relatively far from any nearby villages. In addition, only 10% of workers speak Greek in A 4 and 5% in A 5. Only 30% of workers are registered in both sites, and in these settlements they work for more than 11 hours a day. A 6 scores poorly on all eight criteria. Only 10% of workers are registered and only 5% speak Greek, which makes it extremely difficult to negotiate salaries and working conditions with employers. Working on average 13 hours per day leads to exhaustion and the extremely poor health and safety conditions severely degrade the human rights of migrant workers in this settlement. We then input these data into the Visual PRO-METHEE software in order to obtain the net flow score of each of the six informal settlements and establish a complete ranking based on their risk of labor exploitation (see Table  2 ).
Acknowledgments This work was funded by an  Economic and Social Research Council -Impact Acceleration Account (ESRC -IAA)  grant  [ES/T501992/1 ] and  EPSRC grant  [ EP/ T001569/1 ] to the  Rights Lab of the University of Nottingham. For future correspondence, please contact Ioannis Kougkoulos (ioannis. kougkoulos@gmail. com) or  Doreen Boyd  (doreen. boyd@nottingham. ac. uk). The authors thank the Special Issue Guest Editors, Senior Editor, and three anonymous reviewers for their valuable comments and suggestions which tremendously helped us improve this study. They thank  Airbus Intelligence  for kindly providing their imagery (Pléiades) to the  Rights Lab (University of Nottingham ) without cost, in order to help tackle UNSDG 8. 7 focusing on labor exploitation and modern slavery. They thank the following individuals for their valuable insights and guidance on the development of this work:  Dr.
Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.
Introduction Xmipp is a software package for cryo-electron microscopy (Cryo-EM) and electron tomography (ET), available as a standalone project or via Scipion  [1]  framework. It offers multiple programs for almost all steps of the typical single particle analysis (SPA) processing pipeline and several programs for ET. Originally, Scipion started from the graphical user interface of Xmipp, but it quickly branched off as a separate project of its own. At that time, Scipion and Xmipp  [2]  were available only as a single unit. Scipion was responsible for the inter-package operations between other programs and scripts, while Xmipp provided the programs, methods, and scripts for the actual processing. Since 2018, Xmipp and Scipion are separate software packages. However, Xmipp is still providing some crucial functionality to Scipion and many auxiliary protocols that can be used in the processing pipeline of the Scipion project.
This article provides an overview of the work of the Biocomputing Unit of the CNB-CSIC, Madrid, concerning Xmipp. In the rest of the text, we use the term program to refer to the Xmipp executable and protocol to refer to the Scipion protocol provided by Xmipp. However, both expressions are interchangeable, as the executable is typically at the core of its respective protocol. Unless stated otherwise, this article refers to the latest Xmipp release available at the moment, i. e. , version 3. 21. 06, released on 29 June 2021. The article is divided into three parts. In Section 2, we overview the new programs and protocols added to the Xmipp package since its last dedicated publication in 2013. This section can also be interpreted as an overview of the most active research areas of the Biocomputing Unit of the CNB-CSIC, Madrid.