Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,291
datasets available to search
ShareScore release 0.9.0
Dataset results
1,291 results for “gap”
Assessing the effects of artificial light at night on biodiversity across latitude – Current knowledge gaps
<p><span><b>Aim: </b>Exposure to artificial light at night (ALAN) is a risk factor for organisms. Considering the spread and increasing intensity of night brightness across the globe, and the key role of light at all biological levels, alterations of ecosystems are expected. Yet, we cannot predict the severity of the effects of ALAN in several biomes because little information is available outside the temperate zone. We reviewed current knowledge and identified traits that could be targeted to fill this knowledge gap in order to contribute to the elaboration of a biogeographical framework for the study of ALAN at the global scale.</span></p> <p><span><b>Location: </b>global.</span></p> <p><span><b>Time period: </b>current and next decades.</span></p> <p><span><b>Method: </b>We analysed the latitudinal variation of ALAN and focused on environmental factors that vary with latitude but that have been overlooked. We reviewed biological traits that exhibit latitudinal variation and depend on light and photoperiod and compiled information about the predicted changes of human demography and road networks across different world regions. </span></p> <p><span><b>Results:</b> Cloud cover amplifies ALAN far away from urbanized areas. Because of the higher frequency of overcast sky nights, exposure effects may be stronger both at high latitudes and across a large fraction of the intertropical zone, though at different times of the year. Intertropical biomes host the largest fraction of global biodiversity. Although currently they are not the most exposed to ALAN, their human populations are growing, and urbanized areas and road networks are expanding. Hence, ALAN could have strong ecological consequences, with cloud cover as an aggravating factor.</span></p> <p><span><b>Perspectives: </b>Knowledge gaps currently limit our ability to predict the effects of ALAN in different biomes. Therefore, it will be important to start investigating the consequences of this novel environmental factor across the globe, in order to develop a relevant theoretical framework.</span></p>
Data for: Improving routine childhood immunisation outcomes in low- and middle-income countries: An evidence gap map
<p><strong>Objective</strong>: To support evidence-informed decision-making, we created an evidence gap map (EGM) to characterise the evidence base on the effectiveness of interventions in improving routine childhood immunisation outcomes in low- and middle-income countries (LMICs).</p> <p><strong>Methods</strong>: We developed an intervention-outcome matrix with 38 interventions and 43 outcomes. We searched academic databases and grey literature sources for relevant impact evaluations (IEs) and systematic reviews (SRs). Search results were screened on title/abstract. Those included in the title/abstract were retrieved for a full review. Studies meeting the eligibility criteria were included and data were extracted for each included study. All screening and data extraction was done by two independent reviewers. We analysed these data to identify trends in the geographic distribution of evidence, the concentration of evidence across intervention and outcome categories, and attention to vulnerable populations in the literature.</p> <p><strong>Results: </strong>We identified 309 studies, comprising 226 completed IEs, 58 completed SRs, 24 ongoing IEs, and 1 ongoing SR. Evidence from IEs is heavily concentrated in a handful of countries in Sub- Saharan Africa and South Asia. Among interventions, the most frequently evaluated are those related to education and material incentives for caregivers or health workers. There are gaps in the study of non-material incentives and outreach to vulnerable populations. Among outcomes, those related to vaccine coverage and health are well covered. However, evidence on intermediate outcomes related to health system capacity or barriers faced by caregivers is much more limited.</p> <p><strong>Conclusions:</strong> There is valuable evidence available to decision-makers for use in identifying and deploying effective strategies to increase routine immunisation in LMICs. However, additional research is needed to address gaps in the evidence base.</p>
Meta-analysis of Antarctic phylogeography reveals strong sampling bias and critical knowledge gaps
<p>Much of Antarctica's highly endemic terrestrial biodiversity is found in small ice-free patches. Substantial genetic differentiation has been detected among populations across spatial scales. Sampling is, however, often restricted to commonly-accessed sites, and we therefore lack a comprehensive understanding of broad-scale biogeographic patterns, which could impede forecasts of the nature and impacts of future change. Here, we present a synthesis of published genetic studies across terrestrial Antarctica and the broader Antarctic region, aiming to identify current biogeographic patterns, environmental drivers of diversity, and future research priorities. A database of all published genetic research from terrestrial fauna and flora (excl. microbes) across the Antarctic region was constructed. This database was then filtered to focus on the most well-represented taxa and markers (mitochondrial COI for fauna, and nuclear ITS for flora). The final dataset comprised 7222 records, spanning 153 studies of 335 different species. There was strong taxonomic bias towards flowering plants (52% of all floral data sets) and springtails (54% of all faunal data sets), and geographic bias towards the Antarctic Peninsula and Victoria Land. Recent connectivity between the Antarctic continent and neighbouring landmasses, such as South America and the Southern Ocean Islands (SOIs), was inferred for some groups, but patterns observed for most taxa were strongly influenced by sampling biases. Above-ground wind speed and habitat heterogeneity were positively correlated with genetic diversity indices overall, though environment was a generally poor predictor of genetic diversity. The low resolution and variable coverage of data may also have reduced the power of our comparative inferences. In the future, higher-resolution data, such as genomic SNPs and environmental modelling, alongside targeting sampling of remote sites and under-sampled taxa, will address current knowledge gaps and greatly advance our understanding of evolutionary processes across the Antarctic region.</p>
Figure 5 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 5. Complex ecological network constructed from information about interactions between frugivorous birds (triangles) and non-native plants (circles) in the Brazilian Atlantic Forest (A) and Cerrado (B). Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Names in triangles and circles represent are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Figure 3 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 3. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Atlantic Forest. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Figure 4 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 4. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Cerrado. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Identifying knowledge gap -- Pergubi workshop series
<p>Choosing research topic and posing research questions can be hard. A good literature search and review are the two main ingredient to identify knowledge/research gap to build a good research question.</p> <p>The slides are available on Slideshare: https://www.slideshare.net/d_erwin_irawan/knowledge-gap-identification.</p> <p>I gave this talk on 06.10.2017 at Aston Braga Hotel, Bandung.</p> <p> </p>
FIGURE 11 in Filling in a biogeographic gap: the first Trichomycteridae from the Parnaíba River basin (Siluriformes: Trichomycteridae)
FIGURE 11 | Geographical distribution of Ituglanis crispim (black star).
GAP interatomic potential for SnOx nanoparticles
<p><strong>Gaussian approximation potential</strong> (GAP) [1] for <strong>SnOx </strong>nanoparticles. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by generating a new database of atomic structures containing:</p> <ol> <li>dimers Sn-Sn, Sn-O, O-O;</li> <li>Sn, SnO, SnO2 bulk structures;</li> <li>surface slabs;</li> <li>nanoparticles (SnO_(93.8-81.3)%, SnO_50%, SnO_(19-3.1)%, Sn)</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [3] using the VASP code [4,5]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [6,7]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and <strong>TurboGAP</strong> [8]. This is the reference publication for this potential:</p> <p><strong>Junjie Shi, Paulina Pršlja*, Benjin Jin, Milla Suominen, Jani Sainio, Hua Jiang, Nana Han, Daria Robertson, Janez Košir, Miguel Caro, and Tanja</strong><br><strong>Kallio*</strong>. "Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials". <em>Small</em> 2024, 2402190</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>LibAtoms: <a href="https://libatoms.github.io/">https://libatoms.github.io</a></li> <li>J.P. Perdew, K. Burke and M. Ernzerhof. Phys. Rev. Lett. 77, 3865 (1996).</li> <li>VASP: <a href="http://vasp.at/">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi/">http://turbogap.fi</a></li> </ol>
Data and Script used in "Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest"
<p>The R code and data provided in this repository allow to reproduce the data carpentry, analysis and visualization of “Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest” (https://doi.org/10.1016/j.scitotenv.2024.173572).</p> <p> </p> <p>Folder structure</p> <p> </p> <p>Data abstracts:</p> <p>Data_abstract_climate.pdf</p> <p>Data_abstract_soil_biotics.pdf</p> <p>Data_abstract_soil_abiotics_openness.pdf</p> <p> </p> <p>Data:</p> <p>Climate_data.xlsx</p> <p>Soil_biotics.xlsx</p> <p>Soil_abiotics_openness.xlsx</p> <p> </p> <p>R Scripts:</p> <p>00-preamble.R loads all required packages</p> <p>01-data-carpentry.R loads all datasets and prepares the analysis of all experimental periods.</p> <p>02-data-analyses-microclimate.R compares understorey air and soil microclimate between forest types and treatments, presents diurnal and seasonal variations and tests the relationship of under- and overstorey openness on microclimate.</p> <p>03-data-analyses-decomposition.R compares decomposition rates and feeding activity between forest types and treatments and models the dependencies of soil biological activity on microclimate and soil abiotic factors.</p> <p> </p> <p>Information of related software and package versions used in the script:<br>R version 4.3.2 (2023-10-31 ucrt)<br>Platform: x86_64-w64-mingw32/x64 (64-bit)<br>Running under: Windows 10 x64 (build 19045)<br>Matrix products: default</p> <p> </p> <p>Contact</p> <p>Please contact me at annalena.lenk@uni-leipzig.de if you have further questions.</p>
Addressing Observational Gaps in Aerosol Parameters using Machine Learning: Implications to Aerosol Radiative Forcing
<p>This dataset represents Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Absorption Parameter (AP) data over Kanpur, India, sourced from AERONET with initial data gaps of approximately 37%, 62%, and 58% respectively. To reduce these gaps, XGBoost, a machine learning model trained with reanalysis and satellite datasets, was employed with optimized hyperparameter tuning. Using AERONET data for training, XGBoost effectively addressed gaps, improving AOD by 10%, SSA by 23%, and AP by 21%.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2022)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 24-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2023. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
A Comparative Analysis of Machine Learning Approaches to Gap Filling Meteorological Datasets (Results Only)
<p>This dataset contains the results from our evaluation of methodologies for filling gaps in meterological data. Variables were chosen to represent a standard set of measurements for purposes typically performed using Weather Stations installed in urban and rural areas. There are 4 dimensions by which we measure and validate each of the gap filling models across a large set of experimental configurations: meteorological variables <strong>TargetVar </strong>(dewpoint, humidity, leaf wetness, temp); <strong>feature_set</strong> (AWS, AWS-ERA5, ERA5, ERA5_Debias, Spatial); 3 types of machine learning algorithms <strong>ML</strong> (linear regression, random forests, LightGBM) combined with 2 non-ML algorithms; and <strong>gap_length</strong>: 1, 4, 36 and 288. A total of 1,720 experiments were conducted: 1,440 machine learning experiments; 160 using a spatial algorithm and 120 experiments using ERA5. For the 3 machine learning experiments, the average result was selected for each of 10 sites for 4 gap sizes (40 results) with the 3 ML models using 3 different feature sets (120 results).</p> <p>Data is provided in both CSV format and as a MySQL dump.</p> <p>Resultsets are accompanied with the SQL expression used to generate the result.</p>
Dataset for "Role of electron-phonon coupling and thermal expansion on band gaps, carrier mobility, and interfacial offsets in kesterite thin-film solar cells"
<p>Dataset for "Role of electron-phonon coupling and thermal expansion on band gaps, carrier mobility, and interfacial offsets in kesterite thin-film solar cells"</p>
Market Gaps and Opportunities Dataset
<p>This data has been collected in two phases, with a view to supporting the analysis of successful OI support service providers and platforms, which are interconnected with INVITE project. The first phase involved a desk review of secondary data sources that are available in the public domain. The second phase was implemented through a series of semi-structured in-depth interviews with respondents who work for either the Enterprise Europe Network (EEN), NineSigma or Steinbeis. The data collected consists of a combination of information extracted from the secondary data sources and information provided by the respondents during the in-depth interviews. In both cases, the data collected are mainly of a qualitative nature and recorded in the form of interview transcripts, containing information regarding how the OI platforms/service providers under study currently operate in terms of their value propositions, target audiences, service offerings (both online and offline) and commercial models.</p>
Dataset for "Open-Circuit Voltage Deficit in Cu2ZnSnS4 Solar Cells by Interface Band Gap Narrowing"
<p><strong>Files in "ExchangeCorrelation": POSCAR_material_Exc</strong></p> <p><em>material</em></p> <ul> <li>CdS </li> <li>CZTS = Cu2ZnSnS4</li> <li>CZTSe = Cu2ZnSnSe4</li> </ul> <p><em>Exchange-correlation functionals</em></p> <ul> <li>PBE</li> <li>RP = revised PBE</li> <li>PBEsol</li> <li>AM05</li> <li>SCAN</li> <li>HSE06</li> <li>6_6: PBE+U (U= 6 eV for Cu d and Zn d)</li> <li>8_8: PBE+U (U= 8 eV for Cu d and Zn d)</li> </ul> <p><strong>Files in "Interface"</strong></p> <p>POSCAR_S_fixed: CZTS/CdS interface, CdS layers were fully relaxed.<br> POSCAR_S_PBE_U: CZTS/CdS interface, the atomic coordinates were relaxed using PBE+U<br> POSCAR_S_SCAN_U: CZTS/CdS interface, the atomic coordinates were relaxed using SCAN+U<br> POSCAR_Se_fixed: CZTSe/CdS interface, CdS layers were fully relaxed.<br> POSCAR_Se_PBE_U: CZTSe/CdS interface, the atomic coordinates were relaxed using PBE+U<br> POSCAR_Se_SCAN_U: CZTSe/CdS interface, the atomic coordinates were relaxed using SCAN+U</p>
Laboratory modeling of gap-leaping and intruding western boundary currents under different climate change scenarios
<p>Western boundary currents (WBCs), such as, the Kuroshio and the Gulf Stream, are very intense currents flowing along the western boundaries of the oceans.<br>WBCs -and their respective extensions- have an important effect on climate because of their huge heat transports, the corresponding air–sea interactions and the role they play in sustaining the global conveyor belt. It is therefore very relevant to analyze WBC dynamics not only through observations and numerical modelling, but also by means of laboratory experiments; to this respect several rotating tank experiments have been performed in recent years.<br>The new laboratory experiments proposed here for the Hydralab+ 19GAPWEBS project are aimed at analyzing the interactions of a WBC with gaps located along the western coast. Examples of such processes include the Gulf Stream leaping from the Yucatan to Florida and the Kuroshio leaping, and partly penetrating, through the South and East China Seas and through the wider gap separating Taiwan to Japan. In the experiments the WBC is produced by a horizontally unsheared current flowing over a topographic beta slope; along the western lateral boundary a sequence of gaps of different widths simulate the openings present in the above mentioned locations.</p>
Mind the gap: The location of the lower edge of the pair instability supernovae black hole mass gap
<p>Inlists and run_star_extras.f90 used for the paper:</p> <p>"Mind the gap: The location of the pair instability supernovae black hole mass gap"</p> <p>datafile1.txt contains a machine readable table for all parameters varied in the paper.</p> <p>This work was done with MESA version 11123 and with the MESASDK version 20180822.</p> <p> </p>
Fig. 1 in Filling the Corallian gap: New information on Late Jurassic marine reptile faunas from England
Fig. 1. Palaeogeographical map of Mid-Oxfordian in the UK (from Bradshaw et al. 1992 and Coe 1995).
Fig. 15 in Filling the Corallian gap: New information on Late Jurassic marine reptile faunas from England
Fig. 15. Jurassic Sub-Boreal Seaway marine reptile species count.
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.