dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, from each of the three most common botanical families in the database, we selected the dominant species: white mustard, bristle oat and common vetch. We evaluated the estimates made at the species level and then those that considered biomass classes (data not shown). Species-level estimates were slightly more accurate for bristle oat and common vetch but remained the same for white mustard. Considering N dilution by biomass class increased EF by 0. 10, decreased RMSE by 2 and decreased MD by 1 kg N ha -1 for bristle oat, while vetch and white mustard evaluations remained the same.
Predicting C concentrations based on experimental data The C concentration in shoots was generally higher than that in roots and ranged from 39. 6 in Hydrophyllaceae to 47. 6. in Linaceae. The C concentration in roots of Poaceae (32. 9%) was lower than those of the other families, and the mean for the entire database was less than 40% against 41. 4% in shoots (Table  4 ). Predicting N mineralized and leached from cover crop residues based on simulation data As expected given STICS equations, ANOVA showed that the percentage of N mineralized was explained mainly by the C:N ratio of the residues (71%), followed by the site (20%) and number of months of N mineralization since termination (3%) (total: 94%). The percentage of N leached from residues was also explained by the C:N ratio, site and number of months, but to a lesser degree (20%, 10% and 7%, respectively).
Percentage of N availability over time from cover crop residues To predict the percentage of N availability over time, MERCI calculated the percentages of N mineralized and N leached as a function of the termination date class and C:N ratio predicted from the measurement of fresh shoot biomass in the field for each species and part. In MERCI, the percentage is applied to the N amount predicted by the linear regressions built from the experimental database for each part of each species. Applied on cover crops with measured N total (962 cases) from the experimental database, we found that the cover crop effect on N availability was in average at 23 kg ha -1 over six month, with a range between -20 to + 80 kg N ha -1. In 90% of the situations, this N availability ranged between 10 and 40 kg N ha -1.
The calculated percentage of N availability on the experimental database ranged from -30 to + 60% and was mainly due to the mineralization levels from residue, between -31 and + 60%, the percentage leached being lower, between 0 and 50% with an average at 6% (Fig. 6 ). As expected, N availability percentage was higher for Fabaceae due to lower C:N ratio and higher mineralized N percentage. These results led to average N availability between 22 and 24 kg N ha -1 depending on the family, with a range by family of 45 kg N ha -1 for the lowest value in Hydrophyllaceae and 101 kg N ha -1 for the highest one in Brassicaceae.
Discussion MERCI: a simple and robust decision-support tool to predict nutrient availability for a wide range of cover crops This study developed the decision-support tool MERCI (http:// www. metho de-merci. fr), which estimates N availability from cover crop residues for a wide range of species and botanical families, as sole crops or in a mixture. Its large database provides reference data for the biomass production and N concentration of 74 species, despite having less data for roots due to the difficulty of collecting them in the field. For species with few data (less than 7 by variable), predictions are based on its botanical family or the entire-database, despite decreased accuracy, as done by  Meunier et al. (2022). To our knowledge, MERCI has no equivalent in the literature, especially one built from such a large database.
Many studies have been performed to estimate or measure N release from cover crops in specific situations  (Hansen et al. 2021; Tonitto et al. 2006; Wittwer et al. 2017 ), but they have not developed models to estimate N release easily in order to adapt N fertilization for the next crop, considering endusers, as recommended by  Prost et al. (2011). Coupling an experimental database with simulation by a crop model has been used in previous studies, and was found satisfactory for developing decision-support tools  (Meunier et al. 2022; White et al. 2017). STICS predictions of crop residue mineralization have been validated for the soils and climates used in the simulation experiment (e. g. Constantin et al. 2011; Coucheney et al. 2015; Jégo et al. 2008; Yin et al. 2020) , which increased confidence in its predictions of N mineralization and nitrate leaching.
2011; Monzerrat Ramírez-Olvera et al. 2018). Unexpectedly, water content in the entire plant was not really explained by the cover crop's growing period duration or termination date, perhaps because all cover crops in the database were terminated before reaching the physiological stages of maturity and senescence, which provokes a decrease in water content. The percentage of root biomass in total biomass was consistent with the literature, with higher percentages for Poaceae species  (Redin et al. 2018; Sievers and Cook 2018; Thorup-Kristensen 2001). The percentage was slightly lower than those in studies of wheat or maize, likely because these studies measured mature crops, while cover crops are often terminated when still immature. The root system of young crops is still developing to support future shoot growth, which explains their higher percentage of root biomass.
This may have been due to the small range of biomass of cover crops in the database, given their short growing period and the scarcity of data for many species. N availability for the next crop estimated on the MERCI experimental database ranged from -20 to 80 kg N/ha. In most cases, the effect was between 10 and 40 kg N ha -1 with higher percentages for legume cover crops. It is consistent with previous studies that found N release from cover crops of 10-56 kg N ha -1 depending on cover crop species  (Hansen et al. 2021 ) and of 28 against 60 kg N ha -1 for cereal rye against vetch  (Singh et al. 2020). Associated impact on mineral N fertilization reduction was estimated by several authors that quantified it between 0 and 87 kg N ha -1  (Andraski and Bundy 2005; Fontaine et al. 2020) , that is consistent with the range of N availability estimated in the MERCI tool.
Accuracy was generally higher when many data points were available for a given species, but MERCI remained sufficiently robust and accurate to estimate the N amount from fresh shoot biomass at the family level, or from the entire database for species with few data points or a rare botanical family. This approach provided high flexibility depending on the data available, as well as initial estimates for new species, for which measurements remain rare. Plant Soil 1 3 Vol:. (  1234567890 ) Limits and possible extensions of MERCI Uncertainties in MERCI were quantified for the estimation steps using the experimental database, while for STICS predictions of N mineralized from cover crop residues, the evaluation of RMSE have been made in previous publications. For STICS predictions of N leaching, some experimental data were evaluated, but it is difficult to quantify its uncertainty because it is difficult to measure experimentally.
Conversely, it assumes that shoots and roots do not interact strongly after burial in the soil or during decomposition. Third, the method allows users to add more species to a mixture easily and to run MERCI for mixtures that contain more than two species. However, the addition of more species should be evaluated to ensure the relevance of MERCI for multispecies mixtures, as only 2% of mixtures in the experimental database had more than two species. Finally, the method considers mineralization of the N in cover crop residues that have been buried. According to the literature, burying crop residues mineralizes their N less slowly than leaving them on the soil surface and induces less N immobilization in the soil  (Abiven and Recous 2007; Chaves et al. 2021; Li et al. 2013). Keeping shoots and roots separate in MERCI allows this effect to be simulated by modifying the N mineralization of only the shoots of each cover crop species and could be useful to assess N dynamics in conservation agriculture systems.
The concept of N dilution was considered to attempt to increase the accuracy of N predictions from dry biomass. Classes of dry biomass were used to represent N dilution, but doing so requires much more data on a wide range of biomass for a given species. This requirement rendered application of this concept to the entire database difficult, and prediction accuracy did not always increase. Addition of new data to the experimental database should indicate whether these biomass classes are justified, depending on the increase in accuracy for most species. Use of MERCI will help to guide its development to simultaneously increase its accuracy and adoption by advisors and farmers. To date, the method has been developed and applied only for temperate climates and a range of soils that correspond to the high variability of soils encountered in Western Europe.
Vol. : (0123456789) The database contains few data on roots in general, increasing uncertainties for N predictions and more data could help predict root N more accurately. Nevertheless, N in roots is a relatively small percentage of total N, so its uncertainty has less influence than that of shoots, except for exported residues. Nevertheless, even with an accuracy of 25% (RMSE as a percentage of the mean), MERCI is useful for advisors and farmers to predict N availability for the next cash crop and potentially adjust N fertilization in consequences, as no such tool was previously available for end-users.
Acknowledgements We thank all partners and providers of data for the MERCI experimental database (https:// metho de-merci. fr/ parte naires), as well as the following partners for financial support:  Chambre Régionale d'Agriculture Nouvelle Aquitaine ,  Arvalis Institut du vegetal, Bordeaux ScienceAgro  and  INRAE. This study was financially supported by the  French Ministry of Agriculture in the MERCI Project  ( AAP 775 CasDAR 2018 ).
Introduction Many biological datasets can be represented as a numeric matrix whose rows correspond to measured features and columns to objects of interest (e. g. , cells, biological specimen). Here we consider settings where for each object we have two or more replicate measurements. Examples include RNA-Seq transcriptomics, mass spectrometry proteomics and microscopy-based cell morphology, where the features are levels of transcripts or proteins, or morphological descriptors of shape and texture of cells or cell compartments. The number of features can be in the thousands, but typically not all of them are informative (some are dominated by noise), and some are redundant of each other (they measure essentially the same underlying, relevant variable, in different ways). In this case, it can be desirable to reduce the dimensionality of the data.
Dimensionality reduction can be considered in supervised and unsupervised settings. Here we focus on the latter. There are two basic, not necessarily mutually exclusive, approaches: one is to introduce a smaller number of new variables that are linear or non-linear functions of the original variables; the other is feature selection. Examples for the first approach employ singular value decomposition (SVD), principal component analyis (PCA)  (Jolliffe, 1986) , and numerous versions of (non-linear) multi-dimensional scaling. As the new variables are smooth functions of the original features, random noise can cancel out. Sometimes they are meaningful "latent" variables. Here, however, we focus on feature selection, which can facilitate interpretation and integration of multiple datasets, and is attractively simple.
Evaluation selected of feature subsets by fraction of explained variance Optionally, to inform data-adaptive stopping in lieu of a predetermined value for the number of selected features, Algorithm 1 FeatSeekR algorithm Input: X, max_features, S 0 (default: S 0 = ∅) Output: S t ⊂ {1,. , p} Initialize: t ← 0 while t ≤ max_features do St ← St \ S t # Multivariate linear regression of each feature not # in the selection S t on those in S t P ← X St (X T St X St ) -1 X T St for i ∈ S do Xi• ← PX i• ŷ ŷ ŷi ← X i• -Xi• X i• ← ŷ ŷ ŷi end for # Add feature with highest F-statistic # between residuals to S t and remove from St. i * ← arg max i F i with i ∈ St S t+1 ← S t ∪ {i * } t ← t + 1 end while we can consider the fraction of variance of the dataset that is explained by the currently selected feature subset.
We first model each feature X i• of the original dataset X as a function of the selected features X St analogous to Equation (1). We then get the fraction of explained variance R 2 i of each feature X i• by calculating: R 2 i = 1 - n j=1 ŷij n j=1 (X ij -Xi ) ( 7 ) where Xi is the mean of feature i. We finally get the fraction of variance explained of the whole dataset by averaging R 2 over all features.
Finding informative features in two-class data We generated a p × n data matrix X ∈ R p×n , where n/2 = 500 observations were divided into two classes and p = 50 features exhibit distinct signal-to-noise ratios. The mean values were assigned as follows: µ 1 ,. , µ n/2 = 1 for observations in class 1, and µ n/2+1 ,. , µ n = 2 for observations in class 2. For each feature i, we generated a vector X i• ∼ N (µ, σ i ), where σ i increased linearly as a function of i from 0. 1 to 2. In this simulation setting, the two classes serve as replicates. We ranked the features using two methods: FeatSeekR and based on their variance. We evaluated the feature selection via the performance of subsequent k-means clustering with k = 2 in recovering the two classes. For this, we calculated the adjusted Rand index  (Morey and Agresti, 1984)  between the clustering result and the known class labels.
Microscopy based image data from combinatorial knockout screens Generic feature sets for microscopy based cytometry sets try to cover a wide range of information, ranging from general features such as intensity quantiles, object shapes  (McQuin et al. , 2018; Pau et al. , 2010)  or more abstract textural features introduced by  (Haralick et al. , 1973). As the produced features are designed to cover as much general information as possible, redundancy can be high. Additionally, not all features capture relevant information in every type of experiment. Consequently, some features are dominated by fluctuations that are irrelevant for the assay at hand, and not reproducible between repeated measurements. Here we used FeatSeekR to identify unique features with reproducible signal between measurements in two biological image datasets.
We used data from  (Laufer et al. , 2013) , who performed combinatorial gene knock-downs in human cells using siRNA, followed by imaging, segmentation and feature extraction using the R package EBImage  (Pau et al. , 2010). A summary of datasets we used is shown in Table  1. Analogous to our idealized example in Figure  1 , the extracted features formed groups of high correlation within and lower correlation between (Figure  S2 ). The grouping was partially interpretable as some groups broadly corresponded to different colour channels (fluorescent labels) or cellular compartments. This supports the idea that the effective dimension of the data matrix is substantially lower than the number of features p = 202 and that feature selection is a plausible approach to these data. We used FeatSeekR to select a set of features that explained more than 70% of the variance of the original dataset.
Mass spectrometry-based proteomics data Next we applied FeatSeekR to spectral features of a proteomics dataset from  (Collins et al. , 2017) , where the authors investigated the reproducibility of a mass spectrometry based proteomics measurement across multiple international sites. In this type of experiment, proteins are usually first digested to peptides, separated via LC-MS and their mass spectra are subsequently recorded. To identify individual peptides, mass spectra are either matched to a database or to a library of spectra of known peptides  (Aebersold and Mann, 2016). Beforehand, features such as retention time, intensities and mass accuracies are extracted. The matching to the reference is then done based on these extracted features  (Röst et al. , 2014). We used measurements of 4 sites as replicates, leading to 99340 peptide assays (observations), 37 features and 4 replicates (see Table  1 ).
We observed that not all of these automatically extracted spectral features are equally reproducible and informative across sites. Furthermore, the dataset consists of several correlated redundant feature clusters. For example peak features related to retention time, distance to the reference library or p-value related features form very distinct clusters (Figure  S3 ). We used FeatSeekR to select the most reproducible features that explained at least 70% of the total variance. Figure  3B  shows that we identified the most reproducible features of the redundant and correlated feature clusters. The selected features cover both peptide retention time, as well as information related to their mass spectra.
Our algorithm finds feature sets of biological datasets that achieve a good trade-off between captured information and redundancy. In practice, feature selection can serve different purposes, such as reduction of storage space and computation time, or better performance of downstream machine learning methods. If FeatSeekR is used to improve performance in a machine learning context, feature selection should be incorporated in the cross validation procedure  (Ambroise and McLachlan, 2002). To guide the selection process, we provide diagnostic tools to analyze and visualize information content in biological datasets, within the FeatSeekR package. The objective that motivates feature selection with FeatSeekR does not lead to a unique optimal selection. Conceptually, it is compatible with multiple selections that are, for practical purposes, equally admissible.
Methods Our research adopted a systematic and criterion-based approach to explore the concepts of digital commons, data cooperatives, and digital federation platforms. The primary sources of our data were academic literature, case studies, policy documents, and regulatory frameworks. To gather these, we followed a detailed methodological process outlined below. We commenced our research with a comprehensive literature search, primarily using databases such as Google Scholar, Scopus, Web of Science, and the IEEE Xplore Digital Library. Our search encompassed a broad range of keywords and combinations thereof, including "digital commons", "data cooperatives", "digital federation platforms", "platform cooperatives", "data governance", and "data sovereignty", among others. The search was not confined to a particular time frame to capture the rich history and development of these concepts.
However, we prioritized recent literature to maintain the relevance and applicability of our research. The titles, abstracts, and keywords of the searched articles were screened based on their relevance to our research topic. Full-text articles that met the initial screening criteria were then downloaded for detailed review. For the selection of case studies, we followed a criterion-based approach. We sought case studies that provide substantial insight into the implementation and impact of data cooperatives, digital federation platforms, and platform cooperatives. We looked for examples that depict their governance structures, operational mechanisms, challenges, and achievements. The case studies were obtained from a variety of sources, including academic articles, reports from research institutions, grey literature, and online databases dedicated to platform cooperatives and digital commons.
The selected case studies were then used to inform the analysis and provide real-world evidence for the discussions and arguments presented in the research. In terms of analysis, we employed a thematic approach. Once the relevant literature and case studies were identified, we extracted and synthesized information related to the key themes of our research. We mapped the relationships between the key concepts of digital commons, data cooperatives, and digital federation platforms, and highlighted how they can contribute to more equitable and sustainable digital ecosystems. We also performed a critical analysis of the successes, challenges, and limitations associated with these concepts, thereby addressing the need for a balanced evaluation. Moreover, we explored how the structures and principles of digital commons, data cooperatives, and digital federation platforms can be harnessed to develop a policy framework that addresses the challenges of data concentration and digital inclusivity.
Our analytical methods were systematically structured to develop our policy recommendations. It involved a rigorous process of synthesizing evidence from diverse sources, assessing various policy approaches, and shaping our recommendations accordingly. The step-by-step process we employed is outlined as follows: The initial phase of our analysis involved synthesizing the collected evidence. After carefully reviewing the selected literature and case studies, we extracted and compiled relevant data regarding digital commons, data cooperatives, and digital federation platforms. This compilation was comprehensive, covering diverse dimensions, such as their structure, operation, impact, and challenges faced in implementation. Our evidence synthesis did not just rely on empirical data but also involved a critical interpretation of the findings in relation to the overall research context.
Case Study 4: Decentralized Renewable Energy in Asia (Bangladesh's SOLshare) SOLshare  [71] , a peer-to-peer energy trading platform in Bangladesh, enables rural communities to access affordable, clean energy by connecting solar home systems in a decentralized network. The platform exemplifies the transformative potential of data cooperatives in promoting sustainable development. Nevertheless, the broader adoption of such models requires the development of open standards, APIs, and legal frameworks that support data sharing and collaboration  [72, 73]. Case Study 5: Fintech for Financial Inclusion in South America (Brazil's Nubank) Nubank  [74] , a Brazilian digital bank, has successfully expanded access to financial services for millions of underserved individuals in the region. By leveraging digital technologies and data-driven solutions, Nubank illustrates the potential of innovative platforms to empower small communities and businesses.
Case Study Description Case Study 7: Community Networks in Africa (South Africa's Zenzeleni) Zenzeleni  [79, 80] , a community-owned telecommunications network in South Africa, provides affordable internet access to rural communities by leveraging cooperative ownership and management  [81]. The initiative highlights the importance of local ownership and collaboration in bridging the digital divide. However, regulatory barriers and limited resources impede the expansion of such initiatives, calling for policy interventions and financial support from G20 countries  [82, 83]. Case Study 8: Construction Industry in Bavaria, Germany (Germany's GemeinWerk) GemeinWerk  [3]  proposed the first construction data cooperative in Munich, Germany. The case study of this Bavarian Construction Data Cooperative, which was launched by the Bavarian Construction Industry Association and GemeinWerk Ventures and will be operated by cooperative members, aims to provide small and medium-sized enterprises in the construction industry with access to shared services and construction data via a digital collaborative platform and data cooperative.