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1,311 results for “Literatur”
Open AI Literature 2010-2020 Dataset
<p>The OAIL_10-20 dataset is comprised of OpenAlex records which reproduce the majority of the Web of Science (WoS) records analysed in the course of writing the paper Patterns in the Growth and Thematic Evolution of Artificial Intelligence Research: A Study Using Bradford Distribution of Productivity and Path Analysis, Gupta et al.</p> <p>This paper aims to utilise the Bradford distribution to provide a focused analysis of the thematic evolution of research patterns and growth, and applies this analysis to a corpus of AI papers published over the 10 years between 2010 and 2020. </p> <p>We provide this dataset to allow for researchers to reproduce the findings using open science.</p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
Literature survey of seagrass disturbance-recovery studies up to May 2022
To provide context for the seagrass recovery experiment (SRE) a literature synthesis was conducted on prior studies of seagrass disturbance and recovery. The synthesis provided evidence that 1) less than half (47%) of all seagrass species have been included, 2) were primarily on monospecific meadows of Zostera, 3) where experimental, mostly done on small spatial scales (median = 0.25 m2), and 4) that there is a positive, non-linear relationship between recovery time and disturbance area. Most meadows had some, if not full recovery during the study period suggesting that seagrass meadows are stable across a broad set of conditions.
IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The chapter on<em> Examples of implementing the IPBES data management Policy</em> contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. <strong>Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here:</strong> <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a></p> <p>This session <em>Literature review from the Global Assessment chapter 4 </em>walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment. </p>
IPBES Data Management Tutorials - Session 5.3: Literature access tools
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on literature access tools introduces Research4Life, a tool which provides experts in middle to low income countries access to scientific and grey literature.</p>
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
ZOOOM Literature Review Clean Dataset
<p>The csv file contains the dataset of literature search produced by the ZOOOM EU Funded Project on open software, open hardware, open data business models.</p>
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Systematic Literature Review on Tourism Marketing in the Metaverse
<p>The aim of this research is to investigate tourist marketing within the embryonic context of the metaverse in order to comprehend the building blocks and the primary technologies employed in the sector. For this purpose, a systematic literature review is conducted. The references are extracted in January 2023. The data in this document correspond to the articles finally included in the systematic literature review after the article screening phase.</p> <p>Keywords: tourism marketing, metaverse, technologies, building blocks, SLR (Systematic Literature Review), PRISMA</p> <p> </p>
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Architectural Atmospheres — Literature Record
<p>This dataset is an output of the RESONANCES project. <br>It collects bibliographic entries about four topics:</p> <ul> <li>architectural atmospheres</li> <li>phenomenology in architecture</li> <li>emotions, embodiment, and empathy in architecture</li> <li>the biological basis of atmospheric perception.</li> </ul>
Topic Labels of "Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique"
<p>These are the labels generated with the method proposed in the article <em>"Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique".</em> These labels are for the 100 and 200 DTM topic models, trained both with the whole corpus and with only the COVID-19 period data </p> <p> </p> <p>For the generation of these labels you can go to the original published work or to the linked Zenodo resource.</p>
S37 | LITMINEDNEURO | Neurotoxicants from literature mining PubMed
<p>This is the collection associated with list S37 LITMINEDNEURO on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S37</p> <p>LITMINEDNEURO</p> <p><strong>Neurotoxicants from literature mining PubMed</strong></p> <p>A list of chemicals associated with neurotoxicity compiled through systematic literature mining of PubMed using MeSH terms, compiled by Nancy Baker, Antony Williams (US EPA) and Emma Schymanski (LCSB), details in Schymanski et al (submitted) and on <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">CompTox list</a>. </p> <p>Updated 10/06/2019 to include entries that were registered but not yet publicly available when the previous version was released.</p>
Dataset for paper: A Systematic Literature Review and Recommendations for Ontology-based Support of Digital Forensics
<p>PLEASE, READ THE README.TXT FILE</p> <p>This document describes how to interpret the data and metadata files, and it is licensed under Creative Commons CC BY-NC-AS (https://creativecommons.org/licenses).</p> <p>The file "primary_studies_final_set-DATA.csv" is a CSV file format and contains the raw data extracted from our systematic literature review primary studies. Such data were extracted based on the research questions defined for our study.<br> The file "primary_studies_final_set-METADATA.csv" is a CSV file format and contains the following:<br> - the first row contains two pieces of information: the data type, which might be original or reused;<br> - the second row contains the reused data URL/DOI, which should inform the URL or DOI from which the data was reused, or n/a if the data is original;<br> - the third row contains the date of data generation in the format mm/dd/yyyy;<br> - the fourth row contains 11 elements describing each of the fields of the file "primary_studies_final_set-DATA.csv": the study ID, title, objective, six research questions, and an observation field; and<br> - the fifth row describes the data type of each field of the file "primary_studies_final_set-DATA.csv".<br> The .bib files contain the bibtex entry for the final set of studies.<br> The license.txt file describes the Creative Commons license for this material.</p> <p>We hope you have an excellent read!!</p> <p>Cheers!<br> Thiago, Edson, and Avelino</p>
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Bots in Software Development: A Systematic Literature Review [Data Set]
<p>This repository contains al the artifacts of the research: Bots in Software Development: A Systematic Literature Review </p>
IPBES Invasive Alien Species Assessment, list of literature for Chapter 3
<p>This list of literature represents the literature reviewed for chapter 3 of IPBES thematic assessment of invasive alien species and their control. Please see respective data management report for more details.</p><p>For each data management report, please refer to below links:</p><p>3.2.1 Socio-cultural drivers and social values: <a href="http://doi.org/10.5281/zenodo.8031019">10.5281/zenodo.8031019</a></p><p>3.2.2.1 Regional and national changes in human population density: <a href="http://doi.org/10.5281/zenodo.8031519">10.5281/zenodo.8031519</a></p><p>3.2.2.2 Human migration: <a href="http://doi.org/10.5281/zenodo.8032105">10.5281/zenodo.8032105</a></p><p>3.2.2.3 International crises: armed conflict and humanitarian aid: <a href="http://doi.org/10.5281/zenodo.8032211">10.5281/zenodo.8032211</a></p><p>3.2.2.4 Urbanisation: <a href="http://doi.org/10.5281/zenodo.5553573">10.5281/zenodo.5553573</a></p><p>3.2.3.5 Externalities of negative impacts and cost: <a href="http://doi.org/10.5281/zenodo.8032327">10.5281/zenodo.8032327</a></p><p>3.2.4.1 Research: <a href="http://doi.org/10.5281/zenodo.5717444">10.5281/zenodo.5717444</a></p><p>3.2.4.2 Development of communication technology:<a href="http://doi.org/10.5281/zenodo.8035280">10.5281/zenodo.8035280</a></p><p>3.2.4.3 Breeding and genomic technologies: <a href="http://doi.org/10.5281/zenodo.5591058">10.5281/zenodo.5591058</a></p><p>3.2.5 Policies, governance, and institutions: <a href="http://doi.org/10.5281/zenodo.5717451">10.5281/zenodo.5717451</a></p><p>3.3.1.1 Introductions intentionally or accidentally from agriculture, forestry, fisheries, and aquaculture: <a href="http://doi.org/10.5281/zenodo.8035344">10.5281/zenodo.8035344</a></p><p>3.3.1.2 Fragmentation of ecosystems: <a href="http://doi.org/10.5281/zenodo.8035352">10.5281/zenodo.8035352</a></p><p>3.3.1.3 Creation of anthropogenic corridors: <a href="http://doi.org/10.5281/zenodo.5529361">10.5281/zenodo.5529361</a></p><p>3.3.1.5 Changes in landscape - seascape disturbance regimes (intensification and reduction): <a href="http://doi.org/10.5281/zenodo.8036425">10.5281/zenodo.8036425</a></p><p>3.3.1.6 Landscape and seascape degradation: <a href="http://doi.org/10.5281/zenodo.5533042">10.5281/zenodo.5533042</a></p><p>3.3.2.3 Mining (minerals, metal, oil, fossils fuels): <a href="http://doi.org/10.5281/zenodo.8036498">10.5281/zenodo.8036498</a></p><p>3.3.3.1 Eutrophication and nitrient deposition: <a href="http://doi.org/10.5281/zenodo.8036544">10.5281/zenodo.8036544</a></p><p>3.3.3.2 Other contaminants in water and soil: <a href="http://doi.org/10.5281/zenodo.5587987">10.5281/zenodo.5587987</a></p><p>3.3.3.3 Marine debris: <a href="http://doi.org/10.5281/zenodo.5588374">10.5281/zenodo.5588374</a></p><p>Box 3.8: <a href="http://doi.org/10.5281/zenodo.5588389">10.5281/zenodo.5588389</a></p><p>3.3.4.1 Temperature change: <a href="http://doi.org/10.5281/zenodo.8036828">10.5281/zenodo.8036828</a></p><p>3.3.4.2 Precipitation: <a href="http://doi.org/10.5281/zenodo.8036879">10.5281/zenodo.8036879</a></p><p>3.3.4.3 Climate extremes: <a href="http://doi.org/10.5281/zenodo.5533052">10.5281/zenodo.5533052</a></p><p>3.3.4.4 Carbon dioxide enrichment in air, water: <a href="http://doi.org/10.5281/zenodo.8037007">10.5281/zenodo.8037007</a></p><p>3.3.4.5 Fire regime changes: <a href="http://doi.org/10.5281/zenodo.5591070">10.5281/zenodo.5591070</a></p><p>3.3.4.6 sea level rise: <a href="http://doi.org/10.5281/zenodo.8037086">10.5281/zenodo.8037086</a></p><p>Box 3.9 Assisted colonisation: <a href="http://doi.org/10.5281/zenodo.5535113">10.5281/zenodo.5535113</a></p><p>3.3.5.1 Biotic facilitation: <a href="http://doi.org/10.5281/zenodo.5722695">10.5281/zenodo.5722695</a></p><p>3.3.5.2 Unintended consequences of management (including biological control): <a href="http://doi.org/10.5281/zenodo.8037235">10.5281/zenodo.8037235</a></p><p>3.4.1 Natural hazards: <a href="http://doi.org/10.5281/zenodo.5533488">10.5281/zenodo.5533488</a></p><p>3.5.4 Urbanisation and Pollution: <a href="http://doi.org/10.5281/zenodo.5588440">10.5281/zenodo.5588440</a></p><p>Figure 3.34: <a href="http://doi.org/10.5281/zenodo.7861162">10.5281/zenodo.7861162</a></p><p> </p>
Reviewed literature on the state of conservation planning in Europe
<p><strong>Literature review of European conservation planning studies</strong></p> <p>The data table uploaded here contains reviewed literature as part of the manuscript "An assessment of the state of conservation planning in Europe” by Jung et al. that is currently in Review. In this work we reviewed all available scientific literature broadly dealing with conservation planning in various facets and across realms (terrestrial, freshwater, marine). The database provided here thus provides a comprehensive starting point of all scientific conservation planning studies conducted in Europe up until mid 2023.</p> <p>The dataset is derived from a Scopus literature query conducted on the 23th of September 2022, which resulted in an initial 1459 studies which were further refined and supplemented by evidence known to the authors.</p> <p>---</p> <p># <strong>Description of table columns</strong>:</p> <ul> <li>"ID" = Numeric Identifier of the study</li> <li>"Extent" = Scale the study was conducted, from local, regional, national to European wide</li> <li>"Region" = The broad region with regards to European country</li> <li>"Locality" = Additional detail on the locality of the study if easily available</li> <li>"Realm" = Which realm does the study cover (e.g. Terrestrial, Marine, ...)</li> <li>"Ecosystem.specificity" = Was the study conducted only for specific ecosystems (e.g. Forests)?</li> <li>"Period" = Over which period was the study conducted (Present only, future conditions, both)</li> <li>"Planning.purpose" = What was the purpose of the study?</li> <li>"Policy.relevance" = Specific policy directives or legal documents referred to in the study introduction.</li> <li>"Method" = Which method was used for the planning purpose (i.e., Zonation, Marxan, ...)</li> <li>"Biodiversity.type; = What type of Biodiversity data was included in the study?</li> <li>"Number.of.features" = How many number of features?</li> <li>"Multiple.objectives.or.constraints" = Did the study somehow account for multiple objectives or constraints?</li> <li>"Connectivity" = Was connectivity somehow considered and if so, how?</li> <li>"Costs" = Were socio-economic costs somehow considered?</li> <li>"Stakeholder.involvement" = Were stakeholders involved in the planning exercise at any point?</li> <li>"Authors" = Authors of the study</li> <li>"Title" = The title of the study</li> <li>"Year" = The year it was published</li> <li>"Journal" = The scientific journal were it was published</li> <li>"DOI" = A Digital Object identifier link (can sometimes be missing)</li> <li>"Link" = A link to the journal website (can be missing)</li> <li>"Author.Keywords" = Keywords by the authors given to the study (can be missing)</li> <li>"Index.Keywords" = Keywords captured by SCOPUS for the study (can be missing)</li> <li>"Document.Type" = Type of article</li> <li>"Source" = Derived from SCOPUS or manually added through a snowballing approach?</li> <li>"cite_scientific_May2023" = How often cited in the scientific literature by May 2023?</li> <li>"cite_policy_May2023" = How often cited in policy literatue by May 2023?</li> </ul> <p>---</p> <p>The analysis code supporting the manuscript and analysing the dataset presented here can be found <a href="https://github.com/Martin-Jung/Review_EuropeanConservationPlanning">here</a>. A preprint of the manuscript can be found <a href="http://dx.doi.org/10.31219/osf.io/8x2ug">here</a>.</p>
State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022
This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata.
SHAPE-ID Literature Review dataset: bibliography on IDR/TDR
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of 5040 records of publication metadata (author, abstract, title, keywords, tags etc.), produced for the purposes of the <a href="https://doi.org/10.5281/zenodo.3760417">systematic literature review</a> in the framework of the <a href="https://www.shapeid.eu/">SHAPE-ID</a> project. </p> <p>In the course of the review Project team queried Web of Science (WoS), Scopus and JSTOR databases for records on interdisciplinarity and transdisciplinarity (IDR/TDR). In the case of WoS and Scopus, <a href="https://doi.org/10.5281/zenodo.4034333">complex search strings</a> were created to reflect the main research questions of the Literature Review: different understandings of IDR/TDR and factors and indicators of success or failure of integration of IDR/TDR in research and research policy. JSTOR database offers less advanced data-analytical tools, but the project team decided to include items that have interdisciplinarity or transdisciplinarity in the title, to counterbalance the reported biases against Arts, Humanities and Social Sciences in Scopus and WoS. These three data sources were complemented with bibliographies prepared during the preliminary scoping analysis of IDR/TDR literature. The query results were compiled in reference managers Zotero and Endnote. During data processing the records were normalized and duplicates were removed. </p> <p>Based on systematic review, a sample of the literature had been selected for qualitative analysis. At the same time, the bibliographic metadata was analysed with computationally assisted quantitative methods.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file exported from the Zotero database, and formatted according to the <a href="https://www.zotero.org/support/kb/item_types_and_fields">Zotero metadata model</a>. It contains a collection of 5040 bibliographic records compiled for the purpose of the SHAPE-ID Literature Review.</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.