Find research datasets worth reusing

opencc-by-4.0Jul 2023View details →

Figure 6 in Taxonomic re-evaluation of New World Eptesicus and Histiotus (Chiroptera: Vespertilionidae), with the description of a new genus

Figure 6. (A) plot of multivariate individual scores of New World Eptesicus (red and blue), and Histiotus (black) in the first two canonical variates; analysis was performed using 16 cranial measurements. (B) Vector correlations of craniometric characters with the first two canonical variates. See Table 1 for abbreviations.

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Peatland restoration in Norway – evaluation of ongoing monitoring and identification of plant indicators of restoration success

Norway launched a national action plan on wetland restoration in 2016. So far, 90% of the restoration effort has been on peatland restoration, with about 140 mires restored so far. There are three main restoration goals stated in the action plan: 1) Limit greenhouse gas (GHG) emissions, 2) climate adaptation, and 3) improve ecological condition. Quantifying the outcome of the restoration actions is necessary to evaluate whether the goals of the action plan are met. A vegetation monitoring protocol was suggested before restoration started and has been implemented at five restoration sites. As the peatland restoration effort in Norway is increasing, it is timely to evaluate if the data currently collected can measure peatland restoration outcomes. We evaluate the monitoring protocol based on statistical analyses of the data collected at two sites, describe how indicator species can be identified using generalized composition data used as the basis for classifying habitats in Norway (EcoSyst framework), and suggest the way forward for peatland restoration monitoring in Norway. Data collected according to the monitoring protocol can document changes in species composition at restoration sites but has limitations when the ecological complexity at the sites increases and reference sites are unavailable. We argue that adjusting the monitoring protocol will: 1) Facilitate alignment with existing peatland research; 2) connect better with monitoring programs where data is collected applying EcoSyst framework principles; and 3) enable upscaling to cover the wide variation emerging in peatland restoration.

opencc-zeroDec 2023View details →

opencc-by-4.0Dec 2023View details →

EAMv1 simulation output for evaluating two aerosol process coupling schemes

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains processed output from transient simulations conducted using the F20TRC5-CMIP6 compset. The EAMv1 source code used in the simulations can be found in <a href="https://doi.org/10.5281/zenodo.7995850">Zenodo record 7995850</a>. Scripts used to conduct and analyze the simulations can be found in <a href="https://doi.org/10.5281/zenodo.10371316">Zenodo record 10371316</a>.  The following tarballs contain annually averaged simulation output: <ul> <li>cflx_1_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_1_L72_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L72_F20TRC5-CMIP6.tar.gz</li> </ul> The following split zip archives contain 6-hourly simulation output: <ul> <li>cflx_1_L30_F20TRC5-CMIP6_6h.z*</li> <li>cflx_2_L72_F20TRC5-CMIP6_6h.z*</li> </ul>

opencc-by-4.0Dec 2023View details →

Compressed EAMv1 simulation output for evaluating two aerosol process coupling schemes

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains processed output from transient simulations conducted using the F20TRC5-CMIP6 compset. The EAMv1 source code used in the simulations can be found in <a href="https://doi.org/10.5281/zenodo.7995850">Zenodo record 7995850</a>. Scripts used to conduct and analyze the simulations can be found in <a href="https://doi.org/10.5281/zenodo.10371316">Zenodo record 10371316</a>.  The following tarballs contain annually averaged simulation output: <ul> <li>cflx_1_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_1_L72_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L30_F20TRC5-CMIP6.tar.gz</li> <li>cflx_2_L72_F20TRC5-CMIP6.tar.gz</li> </ul> The following split archives contain 6-hourly simulation output: <ul> <li>cflx_1_L30_F20TRC5-CMIP6_6h.tar.gz.*</li> <li>cflx_2_L72_F20TRC5-CMIP6_6h.tar.gz.*</li> </ul> The split archives can be recombined and untarred using, e.g., the following commands:  <pre><code>${casename}="cflx_1_L30_F20TRC5-CMIP6_6h" cat ${casename}.tar.gz.* | tar xzvf -</code></pre> A copy of the uncompressed and unsplit files can be found through <a href="https://portal.nersc.gov/project/m4359/huiwan/cflx/EAMv1_output_202311_for_Zenodo/">NERSC's web portal</a>.

opencc-by-4.0Dec 2023View details →

SCOPING REVIEW OF EMPIRICAL LITERATURE ON EVALUATION OF EDITORIAL POLICIES SUPPORTING OPEN SCIENCE PRACTICES IN SCHOLARLY JOURNALS

Excel file containing the description of methods & materials used in the scoping review, the whole dataset of included studies, and respective descriptive metadata.

Reporting Database of the INCENTIVE project's monitoring and evaluation activities of the Citizen Science Hubs pilot operation (M16-M31)

The current excel file constitutes the INCENTIVE Reporting Database, meaning a repository of the data that were accumulated in WP4 (described within Deliverable 4.1). The results presented here are the main input for the elaboration of Deliverable 4.2 and Deliverable 4.3.

FIGURE 4 in Global access to nomenclatural botanical resources: Evaluating open access availability

FIGURE 4 (a) Proportion of International Plant Names Index (IPNI) nomenclatural act records that are non-discoverable and (b) their ratio of open to closed access by World Geographic Scheme for Recording Plant Distributions (WGSRPD) level 2.

FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 in Global access to nomenclatural botanical resources: Evaluating open access availability

FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 and their use of open access (OA), coverage of top 80% of the International Plant Names Index (IPNI) dataset (journal abbreviations follow IPNI, asterisk indicates that the title is included in the Directory of Open Access Journals).

FIGURE 2 in Global access to nomenclatural botanical resources: Evaluating open access availability

FIGURE 2 Open access (a) takeup and (b) status for nomenclatural acts recorded in the International Plant Names Index per year of study (2012–2021).

FIGURE 1 in Global access to nomenclatural botanical resources: Evaluating open access availability

FIGURE 1 Flowchart depicting category assignment for (a) takeup of open access and (b) open access status.

opencc-by-4.0Jan 2024View details →

Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments

<h1>Abstract</h1> As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies. <h1>Methodology</h1> This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring: <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows: <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc. <h1>Contribute</h1> Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset. <h1>Publication</h1> This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below: Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a> <h1>Acknowledgements</h1> This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a>

Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid (data evaluation)

This record contains a docker image for data evaluation of AFM-IR data for our publication 'Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid'. The evaluations can be accessed by running the container and accessing the contained Jupyter Lab via a browser. The calculations are contained in 'Bottom_illuminated_PTIR.ipynb'. To run the container (requires docker): 1.download 'container.tar.gz' 2. in the command line, execute 'docker load -i container.tar.gz'. This will return something like 'Loaded image: <image_name>' 3. then 'docker run -p 8888:8888 <image_name>' replacing the brackets with the actual name of the image, such as paper_bottom_illuminated:submission 4.In your command line a link starting in 'http://127.0.0.1:8888/lab?token=...' will appear. Open this link in your browser to access the evaluation.

opencc-by-4.0Oct 2023View details →

Data and code associated with "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America"

Data and code associated with a paper by Yancy et al titled "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America". Metadata is included in files when possible.

Evaluation Data of a Trust-Aware Decentralized Social Network

This dataset includes the evaluation data for the Paper "Trusting Decentralized Web Data in a Solid-based Social Network". Within the ZIP File the following files are included in the dataset: <ul> <li>calculations.csv: All calculated numbers based on the raw data of the conducted empircal user study.</li> <li>questions_translation.csv: A translation of all German questions asked in the survey to English, including a mapping of the question codes to the questions.</li> <li>raw_data.csv: The raw data exported from the used survey tool of the conducted empircal user study.</li> <li>survey.pdf: The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> </ul>

ACHILLES: Ancient and Historical Language Evaluation Set

The dataset used in the <a href="https://sigtyp.github.io/st2024.html">SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages</a>. The task included four problems; problems 1-3 were offered in both <a href="https://codalab.lisn.upsaclay.fr/competitions/16822">constrained</a> and <a href="https://codalab.lisn.upsaclay.fr/competitions/16818">unconstrained</a> tracks on CodaLab, while problem 4 was only a part of the unconstrained track. <ol> <li>POS-tagging</li> <li>Lemmatisation</li> <li>Morphological feature prediction</li> <li>Mask filling <ul> <li>Word-level</li> <li>Character level</li> </ul> </li> </ol> For problems 1-3, data from <a href="http://hdl.handle.net/11234/1-5150">Universal Dependencies v.2.12</a> was used for Ancient Greek, Ancient Hebrew, Classical Chinese, Coptic, Gothic, medieval Icelandic, Latin, Old Church Slavonic, Old East Slavic, Old French and Vedic Sanskrit. Old Hungarian texts, annotated to the same standard as UD corpora, were added to the dataset from the <a href="http://oldhungariancorpus.nytud.hu/en-codices.html">MGTSZ website</a>. In Old Hungarian data, tokens which were POS-tagged PUNCT were altered so that the form matched the lemma to simplify complex punctuation marks used to approximate manuscript symbols; otherwise, no characters were changed. As the ISO 639-3 standard does not distinguish between historical stages of Latin, as it does between other languages like Irish, but it was desirable to approximate this distinction for Latin, we further split Latin data. This resulted in two Latin datasets: Classical and Late Latin, and Medieval Latin. This split was dictated by the composition of the <a href="http://perseusdl.github.io/treebank_data/">Perseus</a> and<a href="http://dev.syntacticus.org/proiel.html"> PROIEL</a> treebanks that served as a source for Latin UD treebanks. Historical forms of Irish were only included in mask filling challenges (problem 4), as the quantity of historical Irish text data which has been tokenised and annotated to a single standard to date is insufficient for the purpose of training models to perform morphological analysis tasks. The texts were drawn from <a href="https://celt.ucc.ie/publishd.html">CELT</a>, <a href="http://corpas.ria.ie/index.php?fsg_function=1">Corpas Stairiúil na Gaeilge</a>, and digital editions of the <a href="http://www.stgallpriscian.ie/">St. Gall glosses</a> and the <a href="https://wuerzburg.ie/">Würzburg glosses</a>. Each Irish text taken from CELT is labelled "Old", "Middle" or "Early Modern" in accordance with the language labels provided in CELT metadata. Because CELT metadata relating to language stages and text dating is reliant on information provided by a variety of different editors of earlier print editions, this metadata can be inconsistent across the corpus and on occasion inaccurate. To mitigate complications arising from this, texts drawn from CELT were included in the dataset only if they had a single Irish language label and if the dates provided in CELT metadata for the text match the expected dates for the given period in the history of the Irish language. The upper temporal boundary was set at 1700 CE, and texts created later than this date were not included in the dataset. The choice of this date is driven by the fact that most of the historical language data used in word embedding research dates back to the 18th century CE or later, and our intention was to focus on the more challenging and yet unaddressed data. The resulting datasets for each language were then shuffled at the sentence level and split into training, validation and test subsets at the ratio of 0.8 : 0.1 : 0.1. A <a href="https://github.com/sigtyp/ST2024/blob/main/list_of_text_sources.md">detailed list of text sources</a> for each language in the dataset, as well as <a href="https://github.com/sigtyp/ST2024/tree/main#data">other metadata</a> and the <a href="https://github.com/sigtyp/ST2024/tree/main#data-format-for-tasks-1-3">description of data formats</a> used for each problem, is provided on the <a href="https://github.com/sigtyp/ST2024/">Shared Task's GitHub</a>. The structure of the dataset is as follows: <pre>📂 morphology (data for problems 1-3) ├── 📂 test       ├── 📂 ref (reference data used in CodaLab competitions)           ├── 📂 lemmatisation           ├── 📂 morph_features           └── 📂 pos_tagging       └── 📂 src (source test data with labels)    ├── 📂 train    └── 📂 valid 📂 fill_mask_word (data for problem 4a) ├── 📂 test       ├── 📂 ref (reference data used in CodaLab competitions)       └── 📂 src (source test data with labels in 2 different formats) ├── 📂 json   └── 📂 tsv ├── 📂 train (train data in 2 different formats) ├── 📂 json   └── 📂 tsv   └── 📂 valid (validation data in 2 different formats) ├── 📂 json   └── 📂 tsv 📂 fill_mask_char (data for problem 4b) </pre> <pre> ├── 📂 test       ├── 📂 ref (reference data used in CodaLab competitions)       └── 📂 src (source test data with labels in 2 different formats) ├── 📂 json   └── 📂 tsv ├── 📂 train (train data in 2 different formats) ├── 📂 json   └── 📂 tsv   └── 📂 valid (validation data in 2 different formats) ├── 📂 json   └── 📂 tsv </pre> We would like to thank Ekaterina Melnikova for suggesting the name for the dataset.

Early Irish Analogy Dataset for Word Embedding Evaluation

An embedding evaluation dataset for Early Irish described in the paper "<a href="https://aclanthology.org/2023.insights-1.10.pdf">Do not Trust the Experts: How the Lack of Standard Complicates NLP for Historical Irish</a>". Traditionally, analogy datasets are based on pairwise semantic proportion, and therefore every question has a single correct answer. Given the high level of variation in historical languages, such a strict definition of a correct answer seems unjustified. Therefore, Early Irish Analogy Dataset follows the <a href="https://vecto.space/projects/BATS/">Bigger Analogy Test Set (BATS)</a> and provides several correct answers to each analogy question.  Morphological and spelling variation data are extracted from the <a href="https://dil.ie/">eDIL</a>, a historical dictionary of medieval Irish. Unlike BATS, no distinction is made between inflection types due to eDIL's structure. The raw data amounted to 2,370 spelling variation and 9,690 morphological variation questions, from which 150 examples were randomly selected for each of the subsets to be comparable in size with the synonym and antonym subsets. The synonym and antonym subsets are translations of the correspondent BATS parts obtained by reverse-searching the eDIL and proofread by four expert evaluators. The dataset includes 98 entries in the synonym subset and 109 entries in the antonym subset, upon which three or more experts agreed.

Increase of active-power-based flexibility (data for KPI evaluation)

There are stored data collected from new EV charging stations – this will be used as an aggregated source of active power-based flexibility procured for system operator (DSO) and managed through non frequency platform.  Data originated from part of the CZ DEMO run directly by ČEZ distribuce called "e – fleet". At Zenodo there are data from all sites (EV charging poles while the first excel sheet contains calculation of the KPI “Increase of active-power-based flexibility”. Detailed explanation on KPI evaluation, data format and main findings from the tests are included in the <a title="link" href="https://www.onenet-project.eu/wp-content/uploads/2024/03/OneNet_D10.5_V1.0.pdf">Deliverable 10.5 (section 2.1.2). </a>

opencc-zeroMar 2024View details →

dryad40/100

Data from: field evaluation: the effect of two transgenic Bt maize events on predatory arthropods in the Huang-Huai-Hai summer maize-growing region of China

To illustrate the impact of genetically modified (GM) Bt maize on the natural enemy communities in the Huang-Huai-Hai summer maize-growing region in China, the abundance of seven common predator groups (Geocoris pallidipennis, Harmonia axyridis, lacewings, Orius sauteri, Propylea japonica, spiders, Staphylinidae) was quantitatively evaluated by planting Bt-Cry1Ab DBN9936 and Bt-Cry1Ab/Cry2Aj Ruifeng 125 events during the growing season from 2016 to 2019. A total of 11,172- 13,739 predators were observed in each varieties during four years, and the abundance of each groups on Bt maize varied between sample dates and among those enemy taxons. The shannon-Wiener diversity index from seven groups showed very similar temporal dynamics and there were not significant differences in Bt and non-Bt maize, showing that Bt maize did not disrupt the stability of predator enemy aggregation in the field. Spiders, H. axyridis, P. japonica, Lacewing, O. sauteri with positive taxon weights after using principal response curve (PRC) method, indicated that Bt maize had a positive effect on the abundances of individual taxa. All this indicating that the two Bt maize hybrids did not adversely affect predator community in the Huang-Huai-Hai summer maize-growing region of China.

opencc-by-4.0Mar 2024View details →

Evaluation of DebIso, a learning platform for systematic debugging

This is the collection of data, produced during the evaluation of DebIso, a learning platform for systematic debugging. See <a href="https://doi.org/10.1145/3677619.3678105">https://doi.org/10.1145/3677619.3678105</a> for the related publication.