Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"

This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study.  <blockquote> Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics. </blockquote> For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>). The data is structured as follows: <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained with the background section as input. These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td>Results from Table 2</td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td>Results from Table 3</td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td>Results from Table 4</td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td>Results from Table 5</td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

Report on Transformers interpretability for Natural Language Processing: A case study on Technical Debt classification

Transformer models have significantly advanced the field of natural language processing (NLP), achieving exceptional results in various tasks. However, these models are often seen as "black boxes", providing limited insight into the factors influencing their predictions. It has become crucial to develop and utilise methods for interpreting and explaining these models to uncover their complex inner workings. This report discusses the latest techniques and tools that aid in a more profound understanding of transformer models within NLP. Additionally, it explores a vital industrial use case: Technical Debt (TD) classification. In this context, the report leverages transformer model interpretability tools and Retrieval Augmented Generation (RAG) to analyse and understand the characteristics of text in Github issues, distinguishing between TD and non-TD. This report thoroughly outlines an approach to improve the transparency and reproducibility of machine learning models, with a special emphasis on TD classification. It integrates the RAG approach and exploits feature attribution techniques, presenting a route to create AI systems that are not only high-performing but also demonstrably trustworthy and comprehensible. Through a detailed examination of word patterns in TD classification and the innovative use of the RAG approach, the research highlights a strong dedication to promoting transparency and responsibility in AI systems, potentially ushering in a new phase in machine learning research that focuses on clarity and dependability.

opencc-by-4.0Oct 2023View details →

Results of the test for the sampling period described in D7.3: Madrid pilot report

This datasets provides further information about section 9.4 Monitorisation and results of D7.3. This public deliverable describes Madrid pilot project report. CARTIF share this dataset with the results of the test carried out during the sampling period regarding particulate matter and nitrogen oxides.

opencc-by-4.0Oct 2023View details →

Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report

The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007–2011: Final Data Report, Kgs. Lyngby: National Museum of Denmark, 122 pp. The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report.  The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted. For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).

opencc-by-4.0Oct 2023View details →

AlphaFold structures reported in "AlphaFold2 Can Predict Single-Mutation Effects"

This contains AlphaFold predictions for X proteins that are found in the Protein Data Bank (PDB), that were used to evalluate AlphaFold's predictions of mutation effects. This includes one set of structures predicted by AlphaFold2.0, using default settings, and one structure for each of 5 models. This also includes structures predicted by the ColabFold version of AlphaFold (6 recycles, 5 models, no template, amber minimization, 4 repeats).There are also additional predicted structures that are found in the PDB that were not analyzed in the paper.There are AlphaFold predictions for three proteins (BFP / RFP, GFP, and PafA), covering either all (BFP/RFP, PafA) or a subset (GFP) of the sequences in three datasets of phenotype measurements from high-throughput experiments.Results are separated into tar files based on whether DeepMind  (AF2.0) or ColabFold implementation was used.Folders under "ColabFold/PDB" are labelled according to a sequence ID, since multiple PDB structures can exist for a single sequence. These sequence IDs can be mapped back to PDB IDs using the information in "seq_id_pdb_id.json".All PDB files have been compressed using Foldcomp (<a href="https://github.com/steineggerlab/foldcomp">https://github.com/steineggerlab/foldcomp</a>). Foldcomp is required to decompress the ".fcz" files in order to recover the ".pdb" files.

opencc-zeroMay 2019View details →

Distribution of invasive alien species of Union concern (Regulation (EU) 1143/2014) in Belgium for the reporting period 2015-2018

Aims and scope Member State authorities are required to report on the distribution in their territory of each of the invasive alien species (IAS) of Union concern. These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). This distribution represents the official reporting under Article 24(1) of R.1143/2014 on invasive alien species for the period 2015–2018. Baseline distribution of these species has previously been reported and published (Adriaens et al. 2018, ). Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (9%), citizen science observatories (68%) and a range of other sources (23%) such as governmental agencies, water managers etc. More specifically the dataset includes: <ul> <li>The citizen science recording portals www.waarnemingen.be and www.observation.be which has a specific alert system for IAS where nature volunteers can report their observations (Adriaens et al. 2018);</li> <li>Data from the Research Institute for Nature and Forest (INBO), the Flemish government institute that coordinates N2000, WFD and BIrd Directive and IAS monitoring in the terrestrial, estuarine and freshwater environment;</li> <li>Data from the Flemish Environment Agency which performs management of muskrat and invasive water plants in Flanders, gathered with a dedicated smartphone app since 2015;</li> <li>Data from the Flemish provinces and Rato vzw that manage water plants, muskrat, giant hogweed etc.;</li> <li>Some smaller datasets from cities;</li> <li>Data from the Brussels Capital Region from the Brussels Environment data portal;</li> <li>Plant inventories of the ‘contrats de rivière’ along watercourses in Wallonia, making use of a dedicated application to collect data directly from the field (fulcrum);</li> <li>The government reporting portals for IAS of the ‘Observatoire wallon de la flore, de la faune et des habitats (Service Public de Wallonie)’;</li> <li>Some validated data from specific datasets on gbif (iNaturalist, Natusfera, Naturgucker).</li> </ul> Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). The mapping was assisted by dedicated software (SMARTIE) which was specifically written for the purpose of aggregating IAS data from various sources. Appropriate selection of records was performed based on the cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA). The technical format is in line with the <a href="http://cdr.eionet.europa.eu/help/ias_regulation/material/IAS-species-distribution-user-manual">guidelines</a> provided to the member states for the compilation of reports on Species Distribution (SD) of Invasive Alien Species of Union concern. File description The dataset contains a shapefiles (T1_Belgium_Union_List_Species.shp) with the distribution of the species of Union Concern at 10km2 (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level. The attributes table contains Cellcode (ETRS grid cell code) and Species (scientific name + authority). Date range The data reflects the distribution of the IAS of Union concern in Belgium in the first reporting period for the EU Regulation hence comprises observations of Union List invasive species between January 2015 (2015-01-01) and December 2018 (2018-12-31).  Validation procedure Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account unless for Chinese mitten crab Eriocheir sinensis, ruddy duck Oxyura jamaicensis, raccoon Procyon lotor, Siberian ground squirrel Tamias sibiricus, sacred ibis Threskiornis aethiopicus, Egyptian goose Alopochen aegyptiaca, Himalayan balsam Impatiens glandulifera, giant hogweed Heracleum mantegazzianum, muskrat Ondatra zibethicus and red-eared slider Trachemys spp. For these species, it was assumed all records were correct as they originate from dedicated sampling (E. sinensis) within research projects, were gathered by public bodies (e.g. muskrat), or represent species that are readily recognizable by people in the field. Data provided by EASIN in the care package and GBIF data were carefully checked. A visual check was performed on the resulting distribution maps by representatives of the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species. Data providers The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF).

openCC (other)Oct 2023View details →

Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan

This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.

openCC (other)Apr 2025View details →

Lake Superior cyanobacterial bloom reports, 2012 - present

Lake Superior is a cold, oligotrophic lake. It is the largest freshwater lake in the world by surface area and the largest of the Laurentian Great Lakes with binational (United States and Canada) and multi-state (Michigan, Wisconsin, Minnesota) borders. Lake Superior is a globally important freshwater resource. Beginning in 2012, researchers and resource managers began receiving reports of cyanobacterial blooms in the western arm of the Lake. Cyanobacterial blooms have been reported nearly every year since. The purpose of this dataset is to collect observations of cyanobacterial blooms for Lake Superior and connecting waters, document patterns in their occurrence, and provide insights into their causes. For this dataset, a cyanobacterial bloom is defined as an aggregation of cyanobacterial biomass in some or all of the water column, which may lead to the occurrence of surface scums or subsurface maximums. This dataset relies on observations, data, photographs, and insights from a wide range of agency staff and individual members of the public with key input from participants in the Lake Superior Algal Bloom and Nutrient Subgroup collaboration. This dataset only includes reported blooms, and likely doesn’t include all actual bloom events. Additionally, there may be more than one report for a single bloom event, depending on its size. For example, in 2018 there was a widespread bloom event which resulted in numerous reports. Interpretation of this dataset should incorporate these nuances. Observations in the dataset were assigned one of four verification statuses to communicate the level of certainty in the cyanobacterial bloom event. Additional information on taxonomy and/or toxin analyses can be made available upon request.

openCC (other)Jun 2025View details →

Harmful Algal Bloom Reports for 405 lakes in New York State (USA), with antecedent weather conditions and trophic status for each lake.

Cyanobacterial harmful algal blooms (cyanoHABs) are often associated with warm water temperatures and low wind speeds, but quantifying thresholds in meteorological conditions is challenging. In an attempt to better quantify antecedent weather conditions associated with cyanoHABs, we assembled a dataset to include cyanoHAB reports from individual lakes, as well as air temperature and wind speed data on the day of, and 5 days preceding each cyanoHAB report. CyanoHAB reports were provided by the New York State Department of Environmental Conservation (NYSDEC) for 405 lakes for an 11 year period spanning 2012-2023. We used 4km gridded surface meteorological data to calculate antecedent weather conditions leading up to each report. The resulting dataset includes 4,208 cyanoHAB reports from lakes of varying trophic states in New York State (USA). We calculated anomalies in daily wind speed and air temperature for each report and the five days preceding it. The dataset is formatted as a wide table, with individual cyanoHAB reports listed by row. Each row provides report-specific data to include the date, site, trophic state of the lake, and the cyanoHAB report status. Each row contains weather data specific to the date and location of each report, with weather variables calculated from the closest grid point for each lake site. We provide 36 columns containing weather variables for each report to include air temperature and wind speed for the day of the HAB as well as the 5 days preceding it. We also provide a 24-year mean value for the day of the year (DoY), as well as the calculated difference between the day in question and the 24-year mean for the DoY.

openCC (other)Jun 2015View details →

Examining Visitor Perspectives about HJA Day: Frequency Report November 2014

Pre- and post-HJA Day surveys were designed to help understand visitor experiences and thoughts about HJA Day 2014. A combined total of 136 individuals registered for HJA Day 2014. Registrants were requested to complete both pre- and post-HJA Day surveys either in person or online. Of these, 76 registrants completed each survey for a 56% response rate of both the pre- and post-HJA Day survey. This frequency report provides an initial look at both pre- and post-HJA Day responses.

openCC (other)Jul 2025View details →

MCR LTER: Coral Reef: River chemistry dynamics in 2018 and 2019; data for Neumann et al., 2025 Scientific Reports

Rivers are the dominant avenue by which terrestrial efflux is transported to marine systems. Human activities on land, such as agriculture and infrastructure development, may impact coral reefs by disrupting historic biogeochemical processes of nearshore reef habitats. To identify how land use impacts rivers, and therefore nearshore lagoons, water chemistry was measured at a series of rivers around the island of Moorea, French Polynesia, in 2018 and 2019. Nitrate, Nitrite, Silicate, Phosphate, and Total Suspended Solids were evaluated at least weekly during sampling campaigns in rainy and dry seasons of both years. TSS was elevated following recent precipitation and negatively associated with watershed size. Phosphate was associated with Total Suspended Solids, presumably because of the phosphate-rich soils on Moorea. Dissolved Inorganic Nitrate was higher during the rainy season in watersheds with a high degree of land clearing. These results reinforce the importance of terrestrial factors for biogeochemistry of nearshore marine systems.

opencc-zeroJan 2020View details →

IODP Expedition 361 Carbonates composite report

This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS] and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.

opencc-by-4.0Feb 2020View details →

Data from Open Research Data: SNSF monitoring report 2017-2018

This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017.  Please cite this data collection as: Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209) Further information is given in the corresponding monitoring report Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123)   Contact Swiss National Science Foundation (SNSF) Open Research Data group E-mail: ord@snf.ch

opencc-by-4.0Dec 2019View details →

Sequence data for Claar et al. 2020 Scientific Reports

Illumina Mi-Seq sequence data of the ITS2 marker (in .fasta format) associated with Claar et al. 2020 Scientific Reports "Chronic disturbance modulates symbiont (Symbiodiniaceae) beta diversity on a coral reef".

opencc-zeroOct 2019View details →

FIG. 2. — Asterolepis alticristata n in Asterolepis alticristata n. sp. (Antiarchi) from the Upper Devonian (Frasnian) of Nunavut, Canada, and a report on the antiarch diversity of the Fram Formation

FIG. 2. — Asterolepis alticristata n. sp., NUFV 897, holotype, anterior median dorsal plate in visceral (A, photograph; A', labeled illustration), cranial (B), left lateral (C), and dermal (D) views. Abbreviations: cd.PMD, area overlapped by the posterior median dorsal plate; cv.ADL, area overlapping the anterior dorsolateral plate; cv.Mxl, area overlapping the mixilateral plate; lc, lateral corner; op.r oblique posterolateral ridge. Scale bar: 5 cm.

opencc-by-4.0Dec 2018View details →

Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"

Supplementary artifacts to "Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities" Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA) Package Contains: - Raw output showing Components, CAWEs, and CVEs per report - Frequency Data (# of reports) for those concerns - ICS Component - Term Dictionary  - HTML versions of reports studied in paper

opencc-zeroMar 2020View details →

IODP Expedition 362 Interstitial water composite report

This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPECTRO, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPECTRO). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.

opencc-zeroMar 2020View details →

IODP Expedition 362 Sample report

Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.

opencc-zeroMar 2020View details →

IODP Expedition 362 Carbonates composite report

This composite report includes data from two analyses (total carbon from \Elemental analysis [CHNS]\ and inorganic carbon from \[Coulometer]\). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.