Find research datasets worth reusing

Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest structure data, Rookery Bay NERR, February 2022 - March 2023

The dataset describes the structure, composition, and condition of mangrove forests in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (Sep 2022). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 69 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the tree has a canopy or is only sprouting at the base of the tree or trunk). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest understory data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the structure and composition of the mangrove forest understory in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest understory data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the structure and composition of the mangrove forest understory in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest structure data, Jobos Bay NERR, March 2022 - August 2022

The dataset describes the structure, composition, and condition of mangrove forests in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 64 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the stem has a leafed canopy or is only sprouting at the base if live). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest coarse woody debris data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the quality and size of coarse woody debris (downed woody debris > 7.5 cm in diameter) for each mangrove forest plot in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane mangrove forest coarse woody debris data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the quality and size of coarse woody debris (downed woody debris > 7.5 cm in diameter) for each mangrove forest plot in Rookery Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane mangrove forest downed woody debris data, Rookery Bay NERR, February 2022 - March 2023

This dataset describes the quantity and size distribution of downed woody debris for each mangrove forest plot in Rookery Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest downed woody debris data, Jobos Bay NERR, March 2022 - August 2022

This dataset describes the quantity and size distribution of downed woody debris for each mangrove forest plot in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). Data was collected along three 20 m transects beginning at each plot center point and heading toward a randomly-selected azimuth. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).

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Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Jobos Bay NERR, April 2024 - December 2024

The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six mangrove forest locations in Jobos Bay National Estuarine Research Reserve (JBNERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Maria (September 2017) were identified and selected for hydrologic monitoring coincident with sites sampled for structural metrics in 2022. One reference site, defined as a site that was observed to be recovering following the hurricane, was selected in black mangrove forest. Two of the six sampling locations were selected to monitor effects of human encroachment on the western boundary of the reserve. These two sites were not coincident with structural sampling plots established in 2022. This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.

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Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Rookery Bay NERR, April 2024 - December 2024

The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six black mangrove forest locations in Rookery Bay National Esturarine Research Reserve (NERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Irma (September 2017) were identified and selected for hydrologic monitoring. The design consists of three sites in mainland/interior black mangroves and three sites on ocean-facing islands, all of which are located on the east side of Hurricane Irma eyewall. In each group, two of the sites selected were considered sites of minimal recovery whereas one site was selected as a reference (location of recovering mangroves). This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.

opencc-by-4.0Jun 2021View details →

Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects

This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects.

opencc-by-4.0Dec 2021View details →

Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project

We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.

opencc-by-4.0Feb 2023View details →

The e-NDP project : collaborative digital edition of the Chapter registers of Notre-Dame of Paris (1326-1504). Ground-truth for handwriting text recognition (HTR) on late medieval manuscripts.

The <a href="https://endp.hypotheses.org/">e-NDP project</a>, funded by the ANR, is led by the <a href="https://lamop.hypotheses.org/6870">LaMOP</a> (Julie Claustre and Darwin Smith). The project's partners are the Archives nationales, the Bibliothèque nationale de France (Department of Manuscripts, Bibliothèque de l'Arsenal), the École nationale des chartes and the Bibliothèque Mazarine. The e-NDP project aims at renewing our knowledge on Notre-Dame de Paris cathedral through the creation of a collaborative digital edition of the registers of its Chapter (1326-1504, AN LL 105-128), the community of 51 canons meeting three times a week on set days to take all administrative, financial and practical decisions pertaining to the cathedral, its estate and the society living in its cloister. This corpus has never been the object of a comprehensive study to understand the workings and history of this urban enclave and powerful community. The collaborative digital edition is based on a process of handwriting text recognition (HTR), tested and supervised by scholars, researchers and engineers combining expertise in Medieval history, paleography, philology and digital humanities. The edition shall allow a better insight into the Chapter’s administration, into its economical and political power within Paris, and the relationships it maintained with other institutions in the city.   Section 1 : The e-NDP ground-truth dataset for Handwriting text recognition. The full e-NDP corpus kept today in the French National Archives and was entirely digitized and described in its <a href="https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_059635">catalog</a> in 2022. The first major goal of the e-NDP projet is to propose a first automatic transcription of the 14k pages composing the 26 chapter registers. To achieve this goal representative samples from each one of the volumes were selected and transcribed in order to train a specialized HTR model able to propose a high quality automatic transcription. The collected ground-truth released on this repository currently has 512 pages from the 26 registers of the cathedral chapter preserved in the National Archives (LL105 - LL128, 1326-1504). The transcriptions were manually completed in two rounds by a group of 12 contributors, historians and paleographers, over the course of 2021-2022 using <a href="https://escriptorium.paris.inria.fr/">eScriptorium </a>as annotation environment.     Ground-truth features : Number of hands : according to our estimates no fewer than 18 main hands were involved in the writing of the registers during the medieval period.  Language : More than 98% of the content of the registers was written in Latin, the rest in French. The exact percentage is hard to estimate because the vernacular language is often used in formulae, notes and comments. It is rare to find entire pages or blocks written in French.  Script family : The registers were written using a Cursive script (ca. late XIIIe - XVIe). Documental typology : The volumes containing the chapter conclusions were conceived to serve as memorial records, but above all as documents for regular use and consultation in the daily practice of administration and management. In diplomatics the notion of "documentary manuscripts" is used to describe this kind of sources also by opposition to books and litterary or normative manuscripts. <table align="center"> <caption>Ground truth statistics</caption> <tbody> <tr> <th>Text units</th> <th>Count</th> </tr> <tr> <td>Pages</td> <td>512</td> </tr> <tr> <td>Annotated regions (see section 2)</td> <td>2448</td> </tr> <tr> <td>Lines of text</td> <td>34231</td> </tr> <tr> <td>Tokens</td> <td>205083</td> </tr> <tr> <td>Characters</td> <td>3320407</td> </tr> </tbody> </table>   Rules of transcription : <ul> <li>The abbreviations have been resolved, both those by suspension (<code>facimꝰ</code> ---> <code>facimus</code>) and by contraction (<code>dñi</code> --> <code>domini</code>). Likewise, those using conventional signs (<code>⁊</code> --> <code>et</code> ; <code>ꝓ</code> --> <code>pro</code>) have been resolved. </li> <li>The named entities (names of persons, places and institutions) have been <code>capitalized</code>. The beginning of a block of text as well as the original capitals used by the notary are also capitalized.</li> <li>The consonantal <code>i</code> and <code>u</code> characters have been transcribed as <code>j</code> and <code>v</code> in both French and Latin.</li> <li>The punctuation marks used in the text: <code>.</code> and <code>/</code> have been transcribed, but the transcription has not been standardized with modern punctuation.</li> <li>Corrections and words that appear cancelled in the manuscript have been transcribed surrounded by the sign <code>$</code> at the beginning and at the end.</li> <li>More specific transcription rules can be found into the file <code>transcription_guidelines.pdf</code></li> </ul>   Section 2. e-NDP Layout Segmentation. Layout segmentation is a compulsory step before HTR recognition in order to distinguish sections and regions inside a document. This process intend to separate interdependant page zones to produce a recognition in a section-sequence order and not in a line-sequence order which mix textual and peri-textual content. The regions of 364 pages (see <code>GT-layout_list</code>) of the e-NDP corpus were annotated using a 5 sections vocabulary (see <code>endp_layout_regions</code>) in order to describe the page distribution in all the 26 volumes : <ol> <li>Block : All the central text blocks, that normally corresponds to the main content called "conclusions" in registers.</li> <li>Liste : List of names of the canons who were present during the meeting. Normally located before the conclusions.</li> <li>Entrée : Marginal notes or entries to inform about the content of conclusions.</li> <li>Date : Paragraph contending the date. Normally at the head of a conclusion, but separate of the main body.</li> <li>Numérotation : Page numbers in roman or arabic. Usually appear in the top corners of the pages.</li> </ol> <table align="center"> <caption>Layout GT statistics</caption> <tbody> <tr> <th>Region</th> <th>Count</th> </tr> <tr> <td>block</td> <td>833</td> </tr> <tr> <td>liste</td> <td>431</td> </tr> <tr> <td>date</td> <td>448</td> </tr> <tr> <td>entrée</td> <td>205</td> </tr> <tr> <td>numérotation</td> <td>531</td> </tr> </tbody> </table>   Section 3. The e-NDP HTR modeling. The e-NDP project has progressively trained several HTR models adapted to work on late medieval cursive in order to accelerate the production of ground truth. Currently the best model delivers an average CER (Character error ratio) of 9.7% in handwriting recognition on the 26 registers (see <code>endp_learning_curve</code>) and can serve as generalist model for other manuscripts of the same period and similar script family. These models and their training implementation details can be found in the project's github <a href="https://github.com/chartes/e-NDP_HTR">repository</a>.  Additionally, the automatic HTR transcriptions of the 26 registers (14k pages, 4.5M tokens) enriched with lexical and semantical information has been the subject of a first <a href="https://nosketch-engine.lamop.fr/#dashboard?corpname=endp">online publication</a> using the NoSketch engine that allows advanced data mining based on the combination of data, metadata and NLP features.    Section 4. Dataset content. This zip dataset contains : - <code>HTR_ground_truth</code> : Two folders containing the jpg / jpeg images and their curated transcriptions in PAGE XML format. - <code>images_docs</code> : 4 files illustrating the different phases of the project (list of GT for layout segmentation, layout ontologie, transcription guideline and HTR evaluation curves)