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Benchmark for the Evaluation of Lexical Semantic Change Detection for Ancient Greek
<p>This repository contains a benchmark of Ancient Greek lemmas which underwent semantic change. It is meant as a support for the evaluation of methods detecting lexical semantic change in Ancient Greek. It was created at the University of Groningen, The Netherlands. A publication will follow soon.</p> <p> </p> <p><strong>1. Overview of the repository</strong></p> <p>This benchmark was created by retrieving and selecting from existing scholarship cases of lexemes which underwent semantic change. The evaluation items are 44 Ancient Greek lemmas, accompanied by the following information (see the column headers in the CSV file):</p> <ul> <li><strong>reference:</strong> the literature source of information about the change;</li> <li><strong>which_change: </strong>an explanation of the change in meaning. NB: the older meaning(s) do not necessarily disappear after the change, but it can happen that the new meaning(s) are added to the existing one(s), increasing the polysemy of the lemma;</li> <li><strong>when_changed: </strong>information about the work(s) or time period in which the change was first recorded; this kind of information was not always available or precise;</li> <li><strong>christian_change:</strong> whether the change is triggered by social, religious, or cultural changes related to the spread of Christianity, according to the scholarship.</li> </ul> <p> </p> <p><strong>2. References</strong></p> <p>The literature used to build this benchmark is the following:</p> <p> BUCK, Carl Darling. A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press, 1949.</p> <p> FINKELBERG, Aryeh. "On the History of the Greek ΚΟΣΜΟΣ." Harvard Studies in Classical Philology (1998): 103-136.</p> <p> GINGRICH, F. Wilbur. "The Greek New Testament as a landmark in the course of semantic change." <em>Journal of Biblical Literature</em> (1954): 189-196.</p> <p> HORKY, Phillip Sidney. "When did Kosmos become the Kosmos." <em>Cosmos in the Ancient World</em> (2019): 22-41.</p> <p> LURAGHI, Silvia. "The verb aréskein in Ancient Greek: Constructions and semantic change." <em>Acta Linguistica Petropolitana. Труды института лингвистических исследований</em> 18-1 (2022): 226-245.</p> <p> </p> <p>These dictionaries of Ancient Greek were also used to double-check the instances of change:</p> <p> LIDDELL, Henry George, and Robert Scott. <em>A Greek-English Lexicon</em>. revised and augmented throughout by. Sir Henry Stuart Jones. with the assistance of. Roderick McKenzie. Oxford. Clarendon Press. 1940.</p> <p> ROCCI, Lorenzo.<em> Vocabolario greco-italiano</em>. Roma. Società editrice Dante Alighieri. 1939.</p> <p> SLUITER, Ineke, and Lucien van Beek, and Ton Kessels, and Albert Rijksbaron. <em>Woordenboek Grieks/Nederlands</em>. 2024. <a href="https://woordenboekgrieks.nl/" target="_blank" rel="noopener">https://woordenboekgrieks.nl/</a></p> <p> </p> <p><strong>3. Acknowledgements</strong></p> <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.<br> <br>We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl/">www.anchoringinnovation.nl</a>.</div> <div> <p> </p> <p><strong>4. How to cite</strong></p> </div> <div>Until there is no publication about this benchmark, please cite the resource as:</div> <div>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim (2024), <em>Benchmark for the Evaluation of Lexical Semantic Change Detection Measures in Ancient Greek</em>, DOI: 10.5281/zenodo.13364555.</div> <div> </div> <div> </div>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Post-remediation evaluation of contaminated site using geophysical methods: Ortophotomosaic Olkusz (Poland) 20220629
<p>The orthophotomap is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is an orthophotomap with a 2.57 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326).</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Telegraf evaluation for AI-SPRINT Monitoring Subsystem
<p>Performance impact evaluation of the AI‑SPRINT Monitoring Subsystem on a system deployment running the AI‑SPRINT Framework with AI applications</p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI
<p>The dataset provided resulted from a driving simulator study conducted by Chemnitz University of Technology (TUC) within work package 3 of the MEDIATOR project. The study focused on the user evaluation of the Mediator system and its functionalities, including the innovative Human Machine interface (HMI). The user evaluation centred on acceptance, trust, usability, comfort and the experience of Transitions of Control (TOCs). The core idea of the Mediator system is to mediate between the human driver and the automated system. Thereby, the Mediator system aims at establishing both as a team that is aware of each other’s strengths, limitations as well as current states in order to achieve safe TOCs, which are actively proposed by the HMI. The main focus of the driving simulator study was on comfort TOCs from manual to automated driving, simulated automation degradation and related TOCs by the human driver, comfort critical situations (i.e., close approach to the rear-end of a traffic jam) as well as the influence of driver characteristics. The provided dataset contains the questionnaire data of 74 German-speaking participants.</p> <p> </p> <p>This document contains information about the study methodology and coding of the variables. For a detailed description, please consult Mediator deliverable D3.3 ‘Results of the MEDIATOR driving simulator evaluation studies’ (Part II – Driving simulator study Germany). Please note that selected passages of this deliverable were adopted (partly in a slightly modified manner) in this document.</p> <p> </p> <p>This dataset is licensed under a <a href="https://spdx.org/licenses/CC-BY-4.0.html">Creative Commons Attribution 4.0 International</a> License.</p> <p> </p> <p>The research leading to this dataset received funding from the European Commission Horizon 2020 programme under the project MEDIATOR (<a href="https://mediatorproject.eu/">https://mediatorproject.eu/</a>), grant agreement number 814735.</p> <p> </p> <p>If you use the dataset, please cite it as: MEDIATOR (2023). MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI. <a href="https://doi.org/10.5281/zenodo.7638299">https://doi.org/10.5281/zenodo.7638299</a></p> <p> </p> <p>For further information, please contact: <a href="mailto:cornelia.hollander@psychologie.tu-chemnitz.de">cornelia.hollander@psychologie.tu-chemnitz.de</a>.</p>
An analyst-created, high-resolution seismic arrival time dataset for evaluating machine learning phase detectors
<p>This data set contains phase arrival times and phase labels for one hour of continuous seismic data recorded at the three-component broadband station WY.YNR on 2014 30 March from 13:00:00 to 14:00:00 UTC. This hour of data follows a M<sub>w</sub> 4.8 occurring at 12:34 UTC in the Yellowstone region and contains many small events close together in space and time. All 687 picks (404 P and 283 S) were made by a seismic analyst trained at the University of Utah Seismograph Stations. The goal was to pick as many reasonable arrivals as possible for evaluating the performance of machine-learning-based phase detectors on continuous data during high seismicity rates. Phase picks were made using the Seismic Analysis Code (SAC; Goldstein and Snoke, 2005) and Pyrocko (Heimann <em>et al</em>., 2017). In general, a 1 to 17 Hz bandpass filter was used.</p> <p>The csv file contains the pick id, phase arrival time in UTC and Unix epoch formats, and the phase labels (P or S).</p>
Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs): dataset
<p>The dataset contains tabular information on the elements of best practice in scholarly publishing found in a set of documents (high-level recommendations and principles, indexation criteria and specific assessment guidelines used on the national and institutional levels). The set of documents subject to analysis (58 items) were identified by the DIAMAS project team members (bibliographic metadata are provided in IPSP-best-practice-documents.xml and IPSP-best-practice-documents.ris).</p> <p>The dataset was compiled by the DIAMAS project team using an analysis matrix that included the general information about the documents (title, issuing entity, scope and purpose, etc.) and the the seven core components of scholarly publishing identified in the Diamond Open Access Action Plan (2022) and revised by the DIAMAS project team.</p> <p>More information about the data collection methodology can be found in the report D3.1 IPSP Best Practices Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs) (<a href="https://doi.org/10.5281/zenodo.7859172">https://doi.org/10.5281/zenodo.7859172</a>), which is based on this dataset.</p> <p> </p> <p><strong>****Dataset contents****</strong></p> <p>IPSPs_best-practices-overview.csv</p> <p>IPSPs_best-practices-overview.ods</p> <p>IPSP-best-practice-documents.xml</p> <p>IPSP-best-practice-documents.ris</p> <p>README.txt</p> <p> </p> <p><strong>****Column headers and field types***</strong></p> <p>Title (original) (text)</p> <p>Title (English) (text)</p> <p>Publication date (date, DD/MM/YY)</p> <p>Last accessed (date, DD/MM/YY)</p> <p>URL (text-web address)</p> <p>Scope (text, controlled)</p> <p>Type of document (text, controlled)</p> <p>Original language (text)</p> <p>Other languages (text)</p> <p>Entity issuing the document (text)</p> <p>Entity responsible for the assessment (text)</p> <p>Scope of the assessment (text, controlled)</p> <p>Scope of assessment: region or country (text)</p> <p>Disciplines’ coverage (text)</p> <p>Periodicity of the assessment (text)</p> <p>Reassessment frequency? If yes: periodicity (text)</p> <p>Benefits linked to the assessment (text)</p> <p>(1) Funding (text)</p> <p>(2) Ownership and governance (text)</p> <p>(3) Open science practices (text)</p> <p>(4) Editorial quality, editorial management and research integrity (text)</p> <p>(5) Technical service efficiency (text)</p> <p>(6) Visibility (including indexation), communication, marketing and impact (text)</p> <p>(7) Diversity, Equity and Inclusion (text)</p>
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization
<p>VetCount<sup>TM</sup> Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm<br> purification device. It consists of two chambers separated by a 10 μM microporous<br> membrane. Untreated semen is applied in one chamber, sperm collection medium<br> in the other. Motile sperm cells are selected by actively swimming through the<br> membrane pores into the medium containing chamber. After 30 min incubation,<br> the sperm collection medium can be aspirated and the purified sperm is ready<br> for further use.<br> In a first experiment we assessed sperm quality and recovery of frozen-thawed semen<br> from six different bulls (n = 6) prior to and after purification with the<br> VetCount<sup>TM</sup> Harvester or BoviPure<sup>TM</sup> gradient centrifugation, a commercial available standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split<br> in two subsamples, and simultaneously purified either with the VetCount<sup>TM</sup> Harvester<br> or BoviPure<sup>TM</sup> gradient centrifugation. Following purification, sperm cells<br> from each treatment group were fluorescently labeled with either MitoTracker<sup>TM</sup><br> Red FM or MitoTracker<sup>TM</sup> Green FM. <em>In vitro</em> matured oocytes were inseminated with<br> equal numbers of red and green labeled sperm. Eighteen hours after fertilization,<br> fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.</p>
Sound samples for the evaluation of source-level blending between violins
<p>The repository includes monophonically rendered sound files used in the source-level blending evaluation. The sound samples provided are recorded from a violin ensemble performance at Detmold Concert House as a part of an investigation on the influence of acoustic environment on the impression of blending [1]. DPA 4099 clip-on microphones were used to capture individual violins in the performance.</p> <p>Each sound sample consists of two violin signals that were rendered by downmixing to a monophonic format at 44.1kHz/16-bit depth. The impression of blending between the two violins in each sample was rated by a group of trained listeners, and the results are provided in the file, 'Sound sample_description'. Please refer to the publication for more details on the performance of the violin ensemble. Also, please cite the publication if these samples are used for scientific evaluations.</p> <p>In addition to the sound samples, three Matlab figures that demonstrate the cluster distribution of MFCC features extracted from sound samples (that are classified into blended and non-blended classes) transformed using Principal component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-Stochastic Neighbourhood Embedding (t-SNE), are included for 3D visualization. This corresponds to Figure 9 in [2].</p> <p> </p> <p> [1] Jithin Thilakan and Malte Kob, “Evaluation of subjective impression of instrument blending in a string ensemble”, Fortschritte der Akustik - DAGA 2021 in Wien, pp. 524-527.</p> <p>[2] Jithin Thilakan, Balamurali BT, Jer-Ming Chen, Malte Kob, “Classification of the perceptual impression of source-level blending between violins in a joint performance”, currently under review at Acta Acustica.</p>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface
<p>Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.</p>
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
Evaluating the precariousness of coral recovery when coral and macroalgae are alternative basins of attraction
When coral and macroalgae are alternative attractors, the trajectory of the benthic community following a major disturbance is shaped in part by whether herbivores keep macroalgae suppressed, leaving reef surfaces in a state suitable for coral colonization. Because macroalgae tend to colonize much faster than coral, an important issue is how close ambient herbivory is to the level where control of macroalgae is lost, that is, the precariousness of the coral-invasible state relative to the switch-point to macroalgae. In Moorea, French Polynesia, reefs dominated by the macroalga Turbinaria ornata remained so for multiple population turnovers, whereas reefs where Turbinaria was removed by a simulated pulse disturbance remained free of macroalgae throughout the 5-yr study. A field experiment revealed that macroalgae was unable to proliferate on disturbed reefs until ambient herbivory was reduced by more than 50%. Low precariousness of the coral-invasible state facilitated a gradual transition to a coral community because it allowed sufficient time for coral to repopulate. Accumulation of coral on disturbed reefs was 3.5 times greater than on undisturbed, Turbinaria-dominated reefs. Long-term persistence of the coral-invasible state was facilitated by high abundances of grazing herbivores that prevent the establishment of macroalgae, whereas persistence of the Turbinaria-dominated state reflected the scarcity of browsing herbivores that consume mature macroalgae. By revealing processes that foster bistability of coral and macroalgae states, our work helps explain the rich dynamical behavior exhibited by coral reef communities in Moorea and elsewhere. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-c
Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods
<p>Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHARP instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM<sub>2.5 </sub>values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM<sub>2.5</sub> values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM<sub>2.5</sub> monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.</p>
An evaluation of the accuracy and speed of metagenome analysis tools
<p>Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity.</p>
Machine Translation Evaluation Dataset for Amharic
<p><strong>Machine Translation Evaluation Dataset for Amharic </strong></p> <p>The dataset contains source sentences in Amharic and English and their corresponding reference translations that were collected using crowd sourcing. These ground-truth sentences are from across different domains such as news headlines, social media, Wikipedia and everyday conversation.</p> <p><br> <strong>Metadata of files in the dataset</strong></p> <p>amen.tsv<br> - Domain: news | wiki | twitter | convo<br> - Source Sentence: Amharic sentence<br> - Reference Translation: English translation<br> - Google Translate: output of Google Translate<br> - Yandex Translate: output of Yandex Translate</p> <p><br> enam.tsv<br> - Domain: news | wiki | twitter | convo<br> - Source Sentence: English sentence<br> - Reference Translation: Amharic translation<br> - Google Translate: output of Google Translate<br> - Yandex Translate: output of Yandex Translate</p> <p><br> <strong>Amharic source and reference translations across domains:</strong></p> <p><em>News: </em>These are news headlines from Ethiopian news websites.<br> <em>Wikipedia: </em>A random sample of sentences from the Amharic Wikipedia.<br> <em>Twitter: </em>Amharic Twitter posts on consumer products.<br> <em>Conversational: </em>Everyday conversational expressions from Amharic native speakers.</p> <p> </p> <p><strong>English source and reference translations across domains:</strong></p> <p><em>News: </em>These are news headlines from <a href="https://en.wikipedia.org/wiki/Portal:Current_events">Wikipedia current events portal</a>.<br> <em>Wikipedia: </em>A random sample of sentences from the English Wikipedia.<br> <em>Twitter: </em>English Twitter posts on global events from Wikipedia current events portal.<br> <em>Conversational: </em>Everyday conversational expressions from English native speakers.</p> <p> </p> <p><strong>Evaluation of two systems that provide Amharic translation</strong></p> <p>The dataset also contains evaluation of two commercial systems: [Google<br> Translate](https://translate.google.com/) and [Yandex<br> Translate](https://translate.yandex.com/). Both systems provide free APIs that<br> users can sign up and get access keys to. The translations for Amharic to English were generated on 14th<br> February 2020. The translations for English to Amharic were generated on 30th March 2020.</p>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins
<p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins” by Wolfgang Esser-Skala, Marius Segl, Therese Wohlschlager, Veronika Reisinger, Johann Holzmann, and Christian G. Huber. See <em>readme.md</em> for further information.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.