dataesr/scientific-paragraphs-categorization huggingface dataset:
The DDRS supplementary database also includes a selected set of research data repositories of generic, national or European provenance. This ensures that a user will always receive a result list, in case the filtering of re3data would result in zero results. Although this approach makes sense from re3data's perspective, it is not helpful with look at the DDRS' use case. Our aim is to equip each user with a selection of suitable research data repositories. To avoid a zero result upon filtering the DDRS database had been supplemented with a set of generic research data repositories suitable for humanities data and referring to the national or European level. However, considering these limitations the decision was still taken to use the re3data database. To our understanding re3data has the potential to grow in data quantity and usage and, for this end, it is a better choice than setting up an own exclusive database for the DDRS.
Our assessment of the future development of re3data also implies a further enhancement of their schema. With more and more established practices and growing use of research data management infrastructures in the humanities, additional properties reflecting this growth will enrich the schema and database. The current concept of the DDRS permits the integration of other databases, but not easily as it would need access to their ElasticSearch servers or with the APIs that are being provided. The following remarks describe in a more technical way the information retrieval of the DDRS from re3data starting with a result list after filtering for two countries affiliations (Germany, France). Figure  9  shows a snippet of the search result of re3data's Elasticsearch server for the following query (it is not possible to provide the full URL as this is not a public API): http://….
/_search?q = institutions. country. raw:DEU AND subjects. text:11 Humanities The search requests re3data to deliver all repositories with German affiliation and included in the DFG subject "11 Humanities". The aforementioned integration of additional sources like the DDRS supplementary database (or even completely different sources) poses rather a challenge in terms of information science than of technology. Different data sources merging into one result for the user requires a mapping on side of the DDRS to ensure that additional properties are associated with the concerned repository. The merged information is done thanks to the use of the re3data's external persistent identifiers, the ones used in their public API, such as "r3d100010677".
The main difference of the consolidated against the simultaneous approach is that the user doesn't see a "filter history". The user only receives the results, and, in some case, this may only be one or no repository. In terms of usability the simultaneous approach is therefore the better choice. • DDRS-ranked results: multiple API retrievals of re3data are stored in the session and ranked for the user as a list. This concept is able to combine aspects of the two other concepts, but it is technically more elaborate and possibly not useful in all cases. In practice a hybrid solution has been implemented. It is a combination of simultaneous retrieval and enrichment by the DDRS database. As the number of questions had been condensed a consolidated retrieval is currently not necessary. This could change if the questionnaire in the beginning would be extended with more questions via the administration section.
A simple example illustrating the search principles using the public API -the user searches for repositories using ARK 27  as PIDs: http://www. re3data. org/api/beta/repositories?pidSystems%5B%5D=ARK and ends up with 24 results. 28 But the user also wants to include the ones using DOI as PIDs in the search as the research data only needs a PID, but not necessarily one or the other: http://www. re3data. org/api/beta/repositories?pidSystems%5B%5D=DOI and ends up with 761 results. After applying the filter for both PID systems at once: http://www. re3data. org/api/beta/repositories?pidSystems%5B%5D=DOI&pidSystems%5B%5D=ARK only 13 results are remaining. However, this last result is confusing as one would like to have all the results using ARK and all results using DOI, but not only the repositories using both ARK and DOI.
Therefore, using the public API, the DDRS would be forced to launch multiple simple queries in order to retrieve meaningful repositories for users. This is a technical reason for liaising with the re3data's team in order to find a solution for this issue. Re3data kindly provided the team with a full ElasticSearch server on their private network which allows the DDRS to make complex queries more easily as seen below. http://…. /_search?q = pidSystems. text. raw:ARK OR pidSystems. text. raw:DOI This provides 772 repositories (761 using DOI, 24 using ARK but including 13 using both) which are more useful to someone looking for a repository using PIDs in general. This issue may also be more complex when other filters are applied, for instance specific technical functionalities or metadata requirements of the repositories.
The third concept would add a ranking mechanism to the results. In other words, the user checks five filters and the results compliant with all five filters would appear on top, the results compliant to only one filter at the bottom of the list. Additionally, the ranking concept could be enhanced by weighting of criteria, for example the availability of a specific author identification system, such as ORCID, 29 is more important than the national affiliation of the repository. This weighted ranking is more sophisticated than the simple ranking and requires a more complex questionnaire approach than the concept currently allows. The current design of the DDRS does not include this option due to the limited number of humanities-specific research data repositories. This may change in the future.
Recommendations for future developments and sustainability The sustainability of the service is crucial. The chosen concept, which builds upon an existing and well-established service -re3data -requires relatively little future maintenance compared to a completely new development. The platform has been developed in such a way that it allows for adaptability (e. g. change of questions, updated repository contact information, additional languages, etc. ) and service extensions for the changing needs of the community. 30 n example for a future update requirement will be updating re3data's metadata schema. At the time of conceptualising the DDRS (2017), re3data uses the 2. 2 version of their schema, 31 but they already present version 3. 0 on their website which will very likely imply changes in re3data's Elasticsearch Fig.
9  94 repositories are listed as result of a query to the re3data Elasticsearch server (as of January 2021). The screenshot shows a snippet with only one repository  30  Although this relies on the resources available for the DDRS within the DARIAH framework. 31 The used schema can be seen here: http://www. re3data. org/api/beta/repository/r3d100011839 configuration. The DDRS therefore has to be able to process retrievals with the new schema in a seamless way as soon as it becomes active. This case is also true if the DDRS wants to include other providers similar to re3data in the retrieval process. An example of a likely future service extension may be related to the recommending functionality. With growing usage of the DDRS it may be useful to aggregate the usage statistics and analyse them in a way to enrich the recommendation results.
Additional service extensions could also cover one or more aspects of the research data life cycle. Our chosen concept emphasises the use case of long-term preservation: the depositing of data for humanities researchers and the curation on the side of the archives. However, the DDRS may also be used in identifying suitable repositories for use in writing a project Data Management Plan (DMP). 32 A logical extension of this service would be to include more resources for data management planning, for example a registry of DMP formats for different humanities disciplines and funding agencies, and/or tools that help with data management planning. Another aspect closely related to depositing data is the promotion and visibility of published data. Basically, two ways in which this could be implemented but they are not really related to the current implementation of the DDRS are feasible.
Firstly, it should be possible for the depositor to simply post links to the deposited dataset on social media platforms, blogs, and project websites. To improve visibility and searchability, another future possibility would be to recommend a common description of 'DARIAH datasets', which means the use of common descriptors and vocabularies like the Backbone Thesaurus. 33 Secondly, DARIAH could consider setting up an Open Humanities data journal. In addition to increasing the visibility of published data, and providing quality assessment of data through peer-review, data journals create an extra incentive for researchers to publish their data because it counts towards their publishing output. Examples of data journals in the humanities are the Journal of Open Humanities Data (JOHD) 34  and the Research Data Journal for the Humanities and Social Sciences.
35 The creation of a DARIAH data journal could be facilitated by the DARIAH Virtual Competence Centres (e. g. VCC3: Scholarly Content Management), for example through the organisation of a DARIAH Working Group to set up and maintain such a data journal. A recent activity in this regard -also an offspring from the Humanities at Scale project -is the Open Methods platform. 36 ther facets of the data life cycle that could be covered, is to find data and process or analyse data. This could be met by an extension of the platform with a data search function and by offering an overview or registry of tools and services, respectively for an overview of service options. 37 However, many of such functionalities are already covered by existing services. Moreover, the more complex the platform services and functionalities, the more resources will be necessary to guarantee the sustainability of the platform.
Since the DDRS is built utilising data and services from other platforms and service providers, it requires minimal maintenance as it does not need to provide a support helpdesk service (FAQs, support documentation may suffice). Issues of updating notwithstanding, this service could be localised and hosted at a number of institutions. High bandwidth is not to be expected as this is just a simple (http) web service. At the current stage of the Humanities at Scale project it seems that the sustainability of developed infrastructure components will be established through the DARIAH ERIC context, of course only under the assumption of a functional and needed service. But this may not be the right scale for a smaller infrastructure component like the DDRS. This approach does not exclude other forms of ensuring sustainability or even a non-DARIAH-branding of the platform.
Conclusions The Data Deposit Recommendation Service (DDRS) is a lightweight web service helping humanities researchers to identify suitable repositories to deposit research data. It has been developed as functional demonstrator within the 37 A few projects are currently working on these. The SSHOC (Social Sciences and Humanities Open Cloud -https://sshopencloud. eu/) project and its SSH Open Marketplace (coordinated by DARIAH) is in development and aims at providing tools and services alongside solutions, training materials, etc. in an effort of contextualisation of the data. The TRIPLE project (https://www. gotriple. eu/) will be the discovery platform for the OPERAS Research Infrastructure (http://operas-eu. org/) and will allow the users to find data, research profiles and projects within the SSH landscape.
Both projects support SSH research in Europe. 38 https:// www. dariah. eu/ activ ities/ proje cts-and-affil iatio ns/ desir/ 39 More details on contributions to DARIAH https:// www. dariah. eu/ tools-servi ces/ contr ibuti ons/ DARIAH-affiliated humanities at Scale project. The complete documentation and code is available at GitHub: https://dariah-eric. github. io/ddrs/ for further development or re-use by third parties. Beside the use case of identifying suitable deposit locations for humanities research data, the DDRS also aims at raising awareness for research data and research data repositories in the humanities. The service has been designed knowingly relying on re3data, a widely known and established registry for research data repositories. This approach shows the potential of re-use and cooperation and is also reflected in the DDRS' flexibility to include other future functionalities beyond the repository recommendation.
Analysis of Sediment Profile Images Individual SPI were analyzed for (1) apparent Redox Potential Discontinuity (aRPD) thickness, (2) numbers of biogenic structures (tubes, burrows, oxic voids, feeding pits), (3) numbers of epi-and infauna, and (4) maximal depths of oxic voids within the sediment column. For the stations presenting a surface sandy layer, the upper limit for the computation of aRPD thickness was set below the transient top coarser sediment layer. Image analyses were achieved using the SpiArcBase software  [60]  and mean values and standard deviations were computed for each station. In addition, all SPIs were visually inspected for the presence of potential traces of bottom trawling. This included the presence of clasts and furrows  [61, 62] , and of a surface puzzle fabric  [63].
In order to account for intense hydrodynamic events, 95th percentiles were used to characterize BSS intensities. The spatial distribution of Bottom Trawling Effort (BTE), within and around the WGMP, was assessed based on vessel Automatic Identification System (AIS) data collected by Global Fishing Watch  [69, 70]. Raw (daily) BTE were available with a 1/100 degree (corresponding to 1. 77 km 2 cells) spatial resolution. For each cell, daily BTE were summed over the whole year preceding the JERICOBENT-5 cruise. Summed BTE were expressed both in terms of h. km -2. y -  and number of times trawled. y -. The conversion between these two units was achieved following Mengual et al. [19, 20].
SIMilarity PERcentage analysis (SIMPER;  [72] ) was used to identify the SPI characteristics contributing the most to between-group differences. All analyses were run on normalized data using the PRIMER ® 6 software package  [73]. Multivariate analyses supported the distinction between a proximal and a distal part in the WGMP (see Section 4. 1). Simple linear regression models were used to assess the significance of the relationships between station depth and both surface sediment and SPI characteristics in the whole WGMP and in the sole distal part to avoid confounding effects resulting from (1) the high between-station variability in the proximal part and (2) major differences in sediment and SPI characteristics between parts. The same approach was used to assess the significance of the relationships between station depths and their coordinates on the first component of both PCAs.
Bottom Shear Stress and Trawling Effort BSS 95th percentiles and summed BTE were mapped using the ArcGIS Desktop ® 10. 6 software. The relationships between BSS 95th percentiles, summed BTE within cells containing sampled stations, and (1) station depth, (2) surface sediment characteristics, and (3) SPI characteristics were assessed using simple linear regression models at the scale of the whole WGMP and/or its sole distal part. Multiple linear regression models were used to test for additive effects of BSS and BTE.
Funding: This work is part of the PhD thesis of  Bastien Lamarque (Bordeaux University ). Bastien Lamarque  was partly supported by a doctoral grant from the  French "Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation". This work was supported by: (1) the  JERICO-NEXT  project (European Union's  Horizon 2020 Research and Innovation program  under grant agreement no. 654410 ), (2) the  VOG  project ( LEFE-CYBER  and  EC2CO-PNEC ), and (3) the  MAGMA  project  (COTE cluster of Excellence   ANR-10-LABX-45 ). It also benefited from additional fundings allocated by the  Conseil Régional Nouvelle-Aquitaine  and the  Agence Française de la Biodiversité. Operations at sea were funded by the  French Oceanographic Fleet.
An analysis reconstructed the impact of the program with household survey data and found that many beneficiaries were wealthier and hold larger landholdings than stated in the selection criteria  (IARNA & FAUSAC, 2013). Also, the government was harshly criticized for its failure to set up a high-quality monitoring system (IARNA & FAUSAC, 2013), even though it did publish several types of relevant data. The evidence on political interference, however, is anecdotal and presented by NGOs and the press. Our study sheds a light on the role of information and information provision for distributional decisions in the direct input subsidy program in Guatemala. Firstly, we examine the claim of political interference through a quantitative study of the factors that influenced distributional decisions in the fertilizer subsidy program during the period 2013-2015.
Secondly, we used a qualitative approach to study which factors influenced information use for distributional decision-making, based on interactions with diverse actors in Guatemala. Based on this evidence, we discuss whether open government data and information have the potential to reduce political interference. The role of information, open (government) data and the digital transformation on decision-making processes is empirically not well established yet  (Jelenic, 2019). We contribute to a further understanding of the potential of open data in government transparency in direct subsidy distribution through a mixed-method approach focused on a case study in Guatemala. Our quantitative approach to investigating the effect of political interference in input distribution can provide a more objective evidence and an assessment of its extent.
The different public entities increasingly published data and information online. In this wave of openness, data reporting on fertilizer distribution were made available to the public as well as data on food security and poverty that went beyond the data normally collected through the census. This allowed us to get detailed data and information to understand in more detail government decision-making. After continued allegations of corruption and clientelism, the fertilizer program ended in 2015 and was substituted by a direct cash subsidy to poor farmers in 2016. 1  MAGA is now distributing vouchers of 200 GTQ (around 27 USD), and farmers are now free to choose the input they need. 2 Allegations of political interference continue until today.
Data sources and panel data analysis We use data on fertilizer distribution for the time from 2012 through 2015 published by MAGA as part of its contribution to the Zero Hunger Pact. These data list the beneficiaries with their names, as well as the bags of fertilizers that were distributed in the 340 municipalities of Guatemala. The data were collected by MAGA and were publicly available during this time on the official webpage of MAGA (https://www. maga. gob. gt/) under the Open Government strategy. The data were eventually removed, and the complete data are not findable anymore on MAGA's current webpage. Part of the data are still available in raw format on archive. org. 3 From these data, we extracted the number of beneficiaries in each municipality for each year. We then estimated the share of beneficiaries as the number of beneficiaries in each municipality with respect to the total population in the municipality, which is our response variable in the analysis.
To examine the determinants of fertilizer distribution we included different control variables in the model. To see whether the distribution is biased we used the results of the presidential elections in 2011 to characterize political affiliation during the government period of 2012-2015. Specifically, we look at which party got the majority of votes in each municipality by using a dummy whether the mayor is from the ruling party. The mayor can be from a different political party than the presidential party. We calculated the vote share percentage of the ruling party in the respective municipality. We obtained election data from the official election report 2011 (TSE, 2011) publicly available at the Guatemalan Supreme Election Tribunal (Tribunal Supremo Electoral). We included additional control variables that function as proxies for the official criteria used to include beneficiaries in the program.
The official criteria for beneficiary selection during the program existence were poverty, food (in-)security and small-scale subsistence production  (ASIES, 2012). We included municipal level data on poverty and data on the rate of chronic and acute undernutrition. As a proxy for the presence for small-scale production in the municipality we included the share of land dedicated to grain production. As small-scale farmers in Guatemala mostly produce maize and beans for subsistence, we assume a higher presence of small-scale farmers where there is a higher total area dedicated to this crop. This is an incomplete proxy, as this could also reflect increased presence of large-scale production. Thus, we also include the Gini coefficient for inequality in land distribution. We use the Gini coefficient as a proxy for the presence of small-scale production in the municipality.
The NDVI uses remotely sensed data to estimate the greenness and vigor of vegetation. The NDVI is used as an index for drought-related stress in agriculture (WMO & GWP, 2016). We included, for each municipality, mean NDVI for June-September, for 2012 through 2015, as a proxy for the presence of an extended dry spell during the rainy season. The rainy season lasts from May to September, peaks in rainfall occur in June and September and the canícula normally occurs in July or early August. This canícula has the biggest effect on agricultural production and food security. Low NDVI values indicate dry vegetation on the ground. To perform the analysis, we had to merge data from different sources and deal with missing values in several of the control variables. We could create a complete dataset only for 240 out of the 340 existing municipalities.
Qualitative data analysis We supplemented the deductive, quantitative analysis on fertilizer distribution with a qualitative and ethnographic approach to explore the role of information and data for distributional decision-making in a more nuanced and contextualized fashion. Complementary data and information were gathered since 2014 through our involvement in a research project financed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Interamerican Institute for Global Change Research. The research was not necessarily directed at answering the research questions of the present manuscript, but they touched upon the topic in different ways (e. g. the topic of fertilizer distribution came up in semi-structured interviews or we could observe how information was generated and used for decision-making for other distributional decisions).