dataesr/scientific-paragraphs-categorization huggingface dataset:
Transmittance spectra were generated from the ratio of the sample spectra with the background spectrum. The detailed experimental condition is given in Table  1. For the Fourier transform, a Mertz-phase correction with a 1 cm -1 phase resolution, a zero-filling factor of 2 and no apodization (boxcar option) were applied to the averaged interferograms, using the procedure included in the Bruker software OPUS package  [31, 32]. The peak-to-peak signal-to-noise ratio (S/N) in the ratioed spectra is around 800. The spectra were calibrated by matching the measured positions of 37 lines of residual H2O in the FTS observed therein to reference wavenumbers available in HITRAN  [10]  with an RMS deviation of 0. 00018 cm -1. The calibration factor was determined with the H2O positions measured in the NH3 spectrum at lower pressure and then applied to all other spectra.
Stakeholder Involvement The review will be conducted as part of the CGIAR Water, Land and Ecosystems funded Sustainable foods through diversity-based practices (Sustainable Foods) project. Sustainable Foods will consult with biodiversity and food system experts including from Bioversity International (now part of the Alliance of Bioversity International and the International Centre for Tropical Agriculture), the United Nations Sustainable Development Solutions Network, Natural History Museum (UK) and Centre de coopération interna-tionale en recherche agronomique pour le développement (France) to seek methodological guidance at key points in the review. For example, 10 stakeholders from across these institutions were convened in a workshop on 3-4 October 2019 to provide feedback and improve the research questions, scope, search strategy, database format, validation process and provisional analysis strategy.
Step 4: Data Extraction and Coding Strategy Qualitative and quantitative data will be extracted from all articles that fulfil all the inclusion criteria. The extracted data will be recorded in a contiguous database specially designed and coded for this review (see Appendix B). Intervention and comparator observations from the same study will be recorded and coded in different individual rows, linked by study (Study_ID) and experiment (Control-Intervention_ID) unique identifiers. This database structure will help to correctly record the data when articles assess multiple intervention or comparators, or more than one taxonomic group or biodiversity metric. Including multiple observations when they are provided in a single article will allow us to use all available data and thus estimate more precise effect sizes than using only a single pair of observations from each article  [40].
We provide clear explanations of how we are going to record and code the extracted data in Appendix B. One reviewer will enter the data and a second reviewer will validate the accuracy and completeness of the data entry, for at least 80% of entries. If there is any disagreement between reviewers, the review team will discuss these to find agreement and make modifications to the database as necessary. The extracted data will be used to assess the primary and secondary questions established. We will extract qualitative data on bibliography information (e. g. , authors, publication year, title); Study_ID (i. e. , numeric ID associated with each unique article, assigned during the screening process); Control-Intervention ID (i. e. , numeric ID associated with each unique intervention or comparator, assigned during the coding process); dominant crop common name (e.
g. , tillage, no tillage, slash and burn); landscape characteristics (e. g. , % agricultural land use, climate); and location of the study (i. e. , country/state and geographical coordinates). The quantitative outcome data that will be recorded include: biodiversity outcome means; biodiversity outcome variance measure (e. g. , standard deviation, standard error of the mean, interquartile range, confidence intervals); number of samples collected; crop yield mean (e. g. , kg/ha, g/plant), crop yield variance (e. g. , standard deviation, standard error of the mean); farm size (in hectares); length of time that the land has been in current state (in years); and duration of the sampling period (in days, from start to finish). Data on biodiversity outcomes and crop yield will be extracted directly from publication text, tables, figures, or supplementary information.
Data from figures will be extracted using GetData Graph Digitizer 2. 26 or WebPlotDigitizer v4. 2. Where data values or units are unclear or not provided (e. g. , the meaning of the error bars in figures), the authors of the corresponding article will be contacted by email. If the authors do not respond, the data entry will be removed. We will extract the data from all interventions and comparators if an article reports outcomes for multiple interventions (e. g. , agroforestry and intercropping), or multiple comparator systems (e. g. , simplified agricultural systems and natural). When outcomes are presented for multiple years (and no average across years is provided), we will include only the data of the last year assessed; otherwise, we will include the average across years. When outcomes are presented for multiple crop stages (e.
g. , flowering and non-flowering) or survey days (e. g. ,  25  and 55 days after sown) for the same year, and the average from across the study is not provided, we will extract all of the outcomes separately. When outcomes are reported at different distances away from the sampling plot, we will only include the data collected closest to the sampling plot. When biodiversity outcomes are reported for multiple taxa groups (e. g. , ants, birds, and bees), functional groups (e. g. , pests and decomposers), sample methods (e. g. , vacuums and pitfall traps), and/or locations, we will enter each one separately in the database. When studies report outcomes for different life stages (e. g. , adult, larvae, pupae), we will record only the most advanced stage. In the case where outcomes are disaggregated by functional group, and further disaggregated by taxonomic group, we will record the outcomes by functional group only.
When a study presents multiple geographical coordinates for the same intervention, comparator, taxa, functional group or biodiversity measure, and these points are in the same region, we will record geographic coordinates of the centroid only. If a study does not present the exact location of the study area (i. e. , in geographical coordinates), we will use the description of the study location to identify the proximate geographic coordinates, making sure to locate the points in agricultural or natural areas as described in each article. After the data extraction process, we will reorganize the database using an R script so that each row of data contains observations from one comparator and an associated intervention from the same article. This procedure will facilitate the calculation of effect sizes.
g. , different sample sizes between interventions and comparators; non-independence of effect sizes within studies). Yes. The intervention can be classified as one of the diversified systems specified in Table  2  No. The intervention was described by the article as "polyculture". -Selection reporting, performance bias, Each observation will be scored as having a "Low", "High", or "Unclear" risk of bias relating to each of the criterion in Table  3. Observations that present "High risk" and/or "Unclear" for two or more assessment criteria will be classified as having a high risk of bias. All the necessary data to score each observation against the validity criteria will be recorded during the data extraction process. The quality and consistency of the recorded information will be checked as described in the data extraction and coding section.
Step 7: Data Synthesis and Presentation Results of the data analysis will synthesize evidence of farm diversity, pesticide and fertiliser management effect on a variety of taxa, functional groups, and across biodiversity metrics. Additionally, the analysis will also quantify the effect of different farm diversification strategies on agricultural yields. A narrative synthesis will be performed in order to summarize, describe and present the results. A descriptive statistic using figures and tables will be conducted to visualize the distribution of the data across countries, taxa groups, functional groups, intervention practices, crop types and quality of observations (assessed against our internal validity criteria). We will use effect sizes based on means to conduct a quantitative meta-analysis of the impact of interventions and comparators on biodiversity outcomes and yield.
We will use Cook's distance or hat values and other established methods to identify potential influential cases and extreme observations  [46]. We will conduct sensitivity analysis to compare the results from the meta-analytical models fitted with all the included studies and the models fitted after excluding outliers. All data and codes used in the analysis will be made available on publication of the results. The compiled database of effect sizes will be made publicly available in raw format and through an interactive and user-friendly website which provides a comprehensive overview of the effects of diversified farming systems on terrestrial biodiversity outcomes and yield. We will also provide the literature included in the analysis for transparency. This would assist researchers and policymakers to assess the effects of different strategies and design improved interventions for their country, crop, farming system, or taxa of interest.
Stage of measurement Experiment_stage Use to record a unique ID for each comparator-intervention pairing that needs to be maintained in analysis. For example: when biodiversity outcomes are reported for multiple taxa groups (e. g. , ants, birds, and bees), functional groups (e. g. , pests and decomposers), sample methods (e. g. , vacuums and pitfall traps), and/or locations, we will enter each one separately in the database, and code with the same number the intervention and comparator outcomes of the same taxa group (e. g. , intervention ants = code: 1; comparator ants = code: 1; intervention birds = code: 2; comparator birds = code:2; intervention bees = code: 3; comparator bees = code:3), functional group (e. g. , intervention pest = code: 1; comparator pest = code: 1; intervention decomposers = code: 2; comparator decomposers = code: 2), sample methods (e.
Acknowledgments: We thank participants of the Sustainable Foods project workshop held 3-4 October 2019 ( Fabrice DeClerck ,  Aline Mosnier ,  Adriana De Palma ,  Roseline Remans ,  Emmanuel Torquebiau  and  Sergio Vîlchez-Mendoza ), together with  Bioversity International interns Emily Cruz  and  Caroline Johnson , who provided valuable feedback on the provisional research questions, search strategy, data extraction and analysis options. We are grateful to three anonymous reviewers whose constructive comments helped improve the manuscript.
1 T. Magnetisation isotherms were collected between 0 and 7 T at 2, 3 and 5 K. ac magnetic susceptibility measurements were performed for both compounds with dc fields from 0 to 3000 Oe and frequencies between 1 to 1500 Hz and 1. 8 to 10 K. Crystallography: Crystallographic data for the structures are summarised in Table  S1. Deposition Numbers 1058715 (for 1-RS), 1058716 (for 1-SS), 2101856 (for 1-RR), 2101853 (for 2-RS), 2101854 (for 2-SS), 2101855 (for 2-RR) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements This work was supported by the  DFG -funded transregional collaborative research centre  SFB/TRR 88  " 3MET ". We acknowledge the  Synchrotron Light Source ANKA  and  Dr. Gernot Buth  for provision of instruments at the SCD beamline for the structure determination of compound 1-SS. AB and AKP acknowledge and support from the  STN programme of the Helmholtz Gemeinschaft. NL thanks the  Alexander von Humboldt foundation for the financial support. Open Access  funding enabled and organized by Projekt DEAL.
However, whether patterns of scientific use of GR actually follow these user-provider assumptions is not a question that has received much attention  [7]. GR provision and use is difficult to follow because GR sampling and exchanges are not centrally administered or recorded. However, the use and citation of sequence data from GR in scientific publications enables a "proxy" view on provider-user relationships and happens to be itself highly relevant to the current CBD discussions. In CBD policy circles, nucleotide sequence data (as well as potentially other data types) are known as "digital sequence information" (DSI)  [8]. Because of the exponential growth and widespread use and reliance on DSI in the biological sciences, the political question of the hour is whether and how benefit sharing from DSI should be required.
This is actually a hypothesis that can be tested with data from open-access public DSI databases, in which the country of origin for the DSI can be found, and via publication databases, where use of DSI can be assessed by proxy through the affiliations of the authors, which can be parsed into geographical locations. While keeping in mind the potential shortcomings and accuracy issues  [10] , here we test this hypothesis and display the results in a free and open data analysis platform with the aim of analyzing whether a real directionality exists from provider country to DSI user country, with LMICs on one side and HICs on the other. The data and their implications are intended to support evidence-based policymaking.
Data Description This article is a companion paper to a Data Note published in the same issue of this journal  [11] , which was also made available as a preprint in bioRxiv. A web application is provided to explore and visualize the dataset  [12] , and, for clarity, we have compiled here a brief excerpt of the dataset described in the Data Note. Most scientific journals and funding agencies require that DSI be made freely available, at the latest, by the time of publication. Submissions of sequence data are required by journals as a condition of publication and rely on the use of unique identifier(s) (called accession numbers [ANs]) generated by a member of the International Nucleotide Sequence Database Collaboration (INSDC). During the sequence submission process, metadata associated with the DSI are also submitted including, where appropriate, the country field (data field "/country") which is defined as "locality of isolation of the sequenced sample indicated in terms of political names for nations, oceans or seas, followed by regions and localities.
" At the time of these analyses, there were 17,816,729 sequences in the INSDC with a country tag. Generally, sequences with country information come from a natural environment. For this study, we did not perform subsequent analyses on the taxonomic distribution of these sequences, which was previously assessed in  [13]. Access to GR is needed to produce DSI. In this article we use the term "provider" to designate the /country information found in the INSDC and indicate the geographical location from where the GR, and thus indirectly the DSI, originated. (Note that "provider" does not reflect where the sequencing was done or the entity that made the research/funding investment). For each of the >17. 8 million ANs, if a publication was listed in the sequence entry page (within the INSDC database), this was added to the dataset as a "primary" publication.
In a parallel step, the European PubMedCentral (ePMC) open-access publication database was text-mined for all >17. 8 million sequence ANs. If a publication listed any of these sequences, it was added to the dataset as a "secondary publication. " A total of 117,483 primary and/or secondary publications were included in this analysis. Publications citing the use of DSI are representative of DSI scientific "use. " Double counts were removed. The associated author metadata from the primary and secondary publications was machine read and parsed. The geographical location of the first author was identified where data quality was sufficient. We note that first author information presents a restricted view of author networks that reflects limitations in the availability of full author information.
As more author data become available, we anticipate that it will be possible to engage in analysis of author networks. This dataset forms the basis of the DSI "user" geographical locations. Additional quality control, data parsing, table merging, and data visualization steps were required that are further explained in the companion Data Note  [11]. We make no further classification under the term "use" because our methods at this stage cannot distinguish among the different types of use of DSI, e. g. , commercial versus non-commercial. On average, we expect that many peer-reviewed publications are more likely to derive from non-commercial research. Provider of DSI User of DSI Provider of DSI used in a publication , where it is the country of origin) for DSI relative to its proportion of users (authors in scientific publications that reference DSI) and the proportion of its DSI cited.
0% income countries such as India and Brazil are in the next wave of providers (Fig. 1 , blue bars). In the 2 years since Rohden et al. [13]  described this trend for a CBD-commissioned study, the pattern has not significantly changed. However, it is important to note that only 14. 6% of all sequences in this INSDC release have country information available. This is down slightly from the 16% observed in the April 2019 global sequence dataset analyzed by Rohden et al. However, this does not necessarily represent a statistical trend. There are multiple factors that could cause this seeming decrease. For example, large deposits of sequences that are not appropriate for country labelling, e. g. , human data (which would only rarely have country information associated with them) could grow the dataset and thus decrease the proportion of country-labelled DSI.
This question (cause of the 16% vs 15%) was not further investigated. Once these DSI are made available by provider countries, the natural question is to ask who is using the DSI, i. e. , scientists sitting in which countries are publishing (which we call "using") and citing DSI in a publication (Fig. 1 , purple bars). To begin to understand the provider-user relationship, it is also important to understand where the DSI that was actually used in a publication comes from, i. e. , which countries provided access to the GR (Fig. 1 , red bars). To summarize, most DSI is being provided and used by HICs; and DSI use and provision often occur in roughly similar proportions. However, some outliers such as Costa Rica and Argentina do show larger differences between providing and using DSI. These data do not, however, support the idea of a unidirectional provider-user dichotomy.
To further understand the real-world provider-user relationship, another angle to examine is the relationship between a country's use of its own "national DSI" (where it was the provider country of the GR) versus its use of DSI from other countries. We call this the "self: world" use ratio where self-use of DSI is divided by use of DSI from all other countries (Fig. 2 ). Although there are significant differences in the number of scientists and the volume of use and reuse between LMICs and HICs, the relationship between a country's use of its own DSI and use of the global DSI dataset (all non-national DSI) is relatively homogenous. A few countries have more "foreign use," i. e. , their national scientists use less relative to non-national scientists, such as Chad and Guyana. However, the vast swaths of blue in the map suggest that the ratio between national and foreign use of a country's DSI is relatively even.
These broad groupings, although imperfect, allow for visual representations that could proxy common trends within UN political discussions. Three significant trends can be observed in Fig. 3. First, the largest group of users (counted by a publication not an individual) in each bar always matches the economic bloc of the country of origin of the DSI. For example, the biggest group of users using G77-sourced DSI is G77-located users. This suggests that users tend to use DSI from their own country/bloc rather than from outside. They research and publish more with "locally sourced" DSI. These data again reject the unidirectional provider-use hypothesis. If that hypothesis were true, then OECD users (from HICs) should be the biggest user group in all 3 bars. Instead, OECD-located users are the smallest group of users of BRICSsourced DSI and the second smallest group of users of G77sourced DSI.
Second, OECD DSI is used nearly 3 times more than either BRICS-sourced DSI or G77-sourced DSI. This is shown by the difference in the height of the bars. This is likely due mostly to the fact that there is simply a lot more OECD-sourced DSI in the databases than DSI from the other blocs. Many negotiators feel strongly that DSI sourced from mega-biodiverse countries is inherently more usable and valuable than DSI from other sources. The data shown here do not support this; otherwise G77 or BRICS DSI, even though fewer in the database, should be used more than OECD DSI. Third, the graph also shows that there are important gaps between the different blocs. There are fewer total DSI-related publications coming from users (authors) located in G77 and BRICS compared to OECD-based users (∼30-40% fewer).
The use of DSI 0 0 3 32. 33 332. 33 On the x-axis is the country of origin of the DSI (i. e. , the "/country" field in the sequence database). On the y-axis are the total number of DSI-using publications, where the colour blocks represent the geographical location of the users (authors) grouped by economic blocs (i. e. , OECD [proxy for developed countries], G77 [proxy for low-income countries], and BRICS [Brazil, Russia, China, India, and South Africa, proxy for large middleincome countries]). The asterisk indicates the subset of the DSI-based publications where the international call for benefit sharing is most focused and where concerns are heightened. An interactive chart is available on the web platform under Graph 5. 1  [12]. and, likely, biological research in general has lower total output as compared to OECD countries.
However, the scale of the data also shows that G77-and BRICS-based authors are still quite scientifically productive. Finally the asterisks indicate the use of G77 or BRICS-sourced DSI by OECD-located users. These users are the primary intended targets of the CBD discussions on DSI and benefit sharing even though these are the smallest blocks of use. It is critical that efforts to try to capture benefit sharing from these 2 small blocks do not disrupt the "self-use" of BRICS and G77-based authors, which could potentially inhibit the biological research community and, with it, opportunities for sustainable economic development in these countries. In other words, policymakers would be wise to ensure that a shot aimed at benefit sharing does not backfire on their own scientists. The fourth question we were able to investigate with this dataset is the geographical interconnectedness between DSI researchers.