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.
8,460
datasets available to search
ShareScore release 0.9.0
Dataset results
8,460 results for “MONITORING”
Data from: Assessing strengths and weaknesses of DNA metabarcoding based macroinvertebrate identification for routine stream monitoring
1) DNA metabarcoding holds great promise for the assessment of macroinvertebrates in stream ecosystems. However, few large-scale studies have compared the performance of DNA metabarcoding with that of routine morphological identification. 2) We performed metabarcoding using four primer sets on macroinvertebrate samples from 18 stream sites across Finland. The samples were collected in 2013 and identified based on morphology as part of a Finnish stream monitoring program. Specimens were morphologically classified, following standardised protocols, to the lowest taxonomic level for which identification was feasible in the routine national monitoring. 3) DNA metabarcoding identified more than twice the number of taxa than the morphology-based protocol, and also yielded a higher taxonomic resolution. For each sample, we detected more taxa by metabarcoding than by the morphological method, and all four primer sets exhibited comparably good performance. Sequence read abundance and the number of specimens per taxon (a proxy for biomass) were significantly correlated in each sample, although the adjusted R2 were low. With a few exceptions, the ecological status assessment metrics calculated from morphological and DNA metabarcoding datasets were similar. Given the recent reduction in sequencing costs, metabarcoding is currently approximately as expensive as morphology-based identification. 4) Using samples obtained in the field, we demonstrated that DNA metabarcoding can achieve comparable assessment results to current protocols relying on morphological identification. Thus, metabarcoding represents a feasible and reliable method to identify macroinvertebrates in stream bioassessment, and offers powerful advantage over morphological identification in providing identification for taxonomic groups that are unfeasible to identify in routine protocols. To unlock the full potential of DNA metabarcoding for ecosystem assessment, however, it will be necessary to address key problems with current laboratory protocols and reference databases.
Data from: Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring
1. Global concern about human impact on biological diversity has triggered an intense research agenda on drivers and consequences of biodiversity change in parallel with international policy seeking to conserve biodiversity and associated ecosystem functions. Quantifying the trends in biodiversity is far from trivial, however, as recently documented by meta-analyses which report little if any net change of local species richness through time. 2. Here, we summarize several limitations of species richness as a metric of biodiversity change and show that the expectation of directional species richness trends under changing conditions is invalid. Instead, we illustrate how a set of species turnover indices provide more information content regarding temporal trends in biodiversity, as they reflect how dominance and identity shift in communities over time. 3. We apply these metrics to three monitoring data sets representing different ecosystem types. In all data sets, nearly complete species turnover occurred, but this was disconnected from any species richness trends. Instead, turnover was strongly influenced by changes in species presence (identities) and dominance (abundances). We further show that these metrics can detect phases of strong compositional shifts in monitoring data and thus identify a different aspect of biodiversity change decoupled from species richness. 4. Synthesis and application: Temporal trends in species richness are insufficient to capture key changes in biodiversity in changing environments. In fact, reductions in environmental quality can lead to transient increases in species richness if immigration or extinction have different temporal dynamics. Thus, biodiversity monitoring programs need to go beyond analyses of trends in richness in favour of more meaningful assessments of biodiversity change.01-Jun-2017
Monitoring food structure during digestion using small-angle scattering and imaging techniques
<p>Monitoring food structure during digestion using small-angle scattering and imaging techniques</p>
Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy
<p>Data underlying the figures in the publication “Monitoring Solid-Phase Reactions in Self-Assembled Monolayers by Surface-Enhanced Raman Spectroscopy”, published in <em>Angew. Chem. Int. Ed.,</em> <strong>2021</strong>, 60, 2–10<strong>.</strong></p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319">https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202102319</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1C</strong>; Zip file containing the numerical data for <em>Figure 1C</em>.</p> <p>The data were obtained from optical numerical simulations using the software <em>Lumerical</em>. The parameters used for the simulations are described in the SI of the publication. The file “OCH04-015_0nm.txt” has been exported from the simulated solution. It includes the distribution of the electric field intensity (|E|^2) in x and y directions at the Au-air interface. The data were then plotted as the electromagnetic enhancement factor in log scale (log|E|^4) using the origin lab software (“OCH04-015.opju”.</p> <p><strong>2. Figure 1D, 1E, 1F</strong>; Zip file containing the numerical data for <em>Figures 1D, 1E</em> and <em>1F.</em></p> <p><strong>Figure 1D:</strong> 100 data files with the general file name:</p> <p>“OCH04-021_3_633nm_300lpermm_10perc_2x30s_300hole_100x_Yyy_Xxx.txt”</p> <p>The yy and xx are different numeric values for each file indicating the position in the 10 x 10 map. And:</p> <p>«OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt” is the dataset of the orange dotted spectrum which was recorded on the planar Au surface.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The Raman spectroscopy data in the files starting with “OCH04-021…” were generated using the Horiba LabRAM Software and the baseline has already been subtracted using this software. The 100 spectra were plotted without further data smoothing (grey spectra) and the average spectrum (black) was generated by using the dedicated function in the Origin Lab software. The orange spectrum originates from «OCH02-072_2_blankAu_2x30s_10perc_633nm_100x_01.txt”. It was smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted.</p> <p><strong>Figure 1E:</strong> The Box Plot was generated using the 100 grey spectra from 1D and applying a Gaussian fit to the three peaks indicated in the figure and extracting the peak positions. Using these peak position data, the box plot was generated using the Origin Lab software.</p> <p><strong>Figure 1F:</strong> The contour plot was generated using the 100 grey spectra from 1D and applying a gaussian fit to the peak indicated in the figure description and extracting the peak heights. Using these peak height data, the contour plot was generated using the Origin Lab software.</p> <p><strong>3. Figure 2</strong>; Zip file containing the numerical data for <em>Figure 2</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalized so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4. Figure 3A, 3C</strong>; Zip file containing the numerical data for <em>Figures 3A</em> and <em>3C</em>.</p> <p>In all text files, there are two columns: The first one is the Raman shift in cm–1 and the second one the intensity in photon counts. The spectra were smoothed with 8 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The average of three spectra was calculated for the spectra with the same y description for the plotted spectra in 3A. Figure 3C was generated by applying a gaussian fit to the three peaks indicated in the figure in the original 12 data sets and extracting the peak heights. The average and standard deviation of the peak height data from the spectra with the same y description was then calculated to generate Figure 3C.</p> <p><strong>5. Figure 4A, 4B</strong>; Zip file containing the numerical data for <em>Figures 4A</em> and <em>4B</em>.</p> <p><strong>4A:</strong> In all text files, there are two columns: The first one is the Raman shift in cm<sup>–1</sup> and the second one the intensity in photon counts. The spectra were smoothed with 10 points using a Savitzky-Golay Filter in Origin Lab and the Baseline was subtracted. The y intensity was normalised so that the Si peak at approx. 950 cm<sup>–1</sup> had the same height. The Raman shift in x direction was shifted so that the Si peak at 300 cm<sup>–1</sup> was at the same position in each spectrum.</p> <p><strong>4B:</strong> The peak positions from <em>Figures 2</em> and <em>4A</em> were used to generate <em>Figure 4B</em>.</p>
Paraquat residues in soya products intended for animal feed (monitoring results from 2018-2019)
<p>Monitoring results for paraquat residues in soya products intended for animal feed. 174 samples were analyzed in 2018 and 2019.</p>
Dataset Used in "Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes"
<p>This is the dataset used in Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” <em>Journal of Quality Technology</em>, Vol. 38(3), pp. 199-216. This data file has 528 data records, two fewer than the 530 number mentioned in the paper.</p>
Figure 1 from: Rutledge C, Fierke M, Careless P, Worthley T (2013) First detection of Agrilus planipennis in Connecticut made by monitoring Cerceris fumipennis (Crabronidae) colonies. Journal of Hymenoptera Research 32: 75-81. https://doi.org/10.3897/jhr.32.4865
Figure 1 - Abandoned emerald ash borer next to the nest entrance of Cerceris fumipennis at a colony in Prospect CT.
Figure 5 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 5 - Non-metric Multidimensional Scaling plot of K2P-distance between MCR sequences of Stenella coeruleoalba (in blue), Delphinus delphis (in red) and Stenella frontalis (in green). Individuals of each species are clearly clustered together, and unidentified samples (in black) stranded along the coasts of Brittany group with one of the three species. Dd280211A (Ds1), Ds130210 (Ds3), Ds230409 (Ds4), Ds250412 (Ds5) and Sc210910 (Ds6) are putatively identified as Delphinus delphis, whereas Ds080410 (Ds2) would more likely belong to Stenella coeruloalba.
Figure 2 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 2 - Organization of the stranding network in Brittany (North West of France) and localization of the stranded specimens used in this study. Numbers indicate the 18 geographic sections of the stranding network in this area. The map was drawn using ArcGIS Desktop: Release 9.3.1 (Environmental Systems Research Institute, Redlands, CA, USA) with WGS 84 coordinates.
Figure 1 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 1 - Numbers of different species of marine mammals stranded along the coasts of Brittany (North West of France) in the period 2003–2012.
Figure 4 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 4 - Examples of marine mammals stranded along the coasts of Brittany and the species-level identifications of which were determined or confirmed thanks to DNA barcoding. A Sample Ms250511, stranded on the "Île de Sein" during May 2011, and identified as a Balaenoptera physalus B Sample Ds160111, stranded on the Ushant Island during January 2011, and identified as a Grampus griseus C Sample Ds130211, stranded on the Ushant Island in February 2011, and identified as belonging to the Delphininae subfamily (putatively identified as a Delphinus delphis on the nMDS plot in Figure 5) D Sample Ds080410 stranded on the Ushant Island during April 2010, and identified as belonging to the Delphininae (putatively identified as a Stenella coeruleoalba on the nMDS plot in Figure 5).
Figure 3 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 3 - Neighbour-Joining tree of major species of marine mammals, based on K2P-distances calculated from 507 bp of COI. All sequences come from the IMMB project on BOLD, and only 5 harbour porpoise and 5 grey seal samples among those of the IMMB project have been included in the analysis.
Figure 6 from: Jung J, Alfonsi E, Méheust E, Fuchs S, Carpentier F, Quillivic Y, Viricel A, Hassani S (2013) The use of DNA barcoding to monitor the marine mammal biodiversity along the French Atlantic coast. ZooKeys 365: 5-24. https://doi.org/10.3897/zookeys.365.5873
Figure 6 - Haplotype network established from the COI sequences of 45 harbour porpoises stranded along the Atlantic coast of France (Appendix 1). Numbers on a line connecting two haplotypes correspond to the sequence position of the mutation differentiating these haplotypes. Two mitochondrial haplogroups appear (black circles - grey circles), that group the same individuals as the haplogroups alpha and beta determined using MCR polymorphisms and described in Alfonsi et al. (2012).
Figure 4 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 4 - Dendrogram shows similarity between the sites, constructed on the basis of Morisita–Horn similarity index.
Figure 1 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 1 - Map of Serbia with monitored aphid flight activities sites. Coordinates of localities: Begeč 2007 (45°13'26"N, 19°36'53"E), Begeč 2008 (45°13'34"N, 19°37'23"E), Glumač 2008 (43°52'27"N, 20°1'1"E), Golija 1 2007 (43°27'45"N, 20°20'58"E), Golija 2 2007 (43°21'58"N, 20°32'12"E), Golija 1 2008 (43°26'25"N, 20°14'56"E), Golija 2 2008 (43°23'36"N, 20°24'53"E), Golija 1 2009 (43°24'6"N, 20°29'38"E), Golija 2 2009 (43°23'48"N, 20°18'31"E), Kotraža 2007 (43°42'7"N, 20°13'49"E), Kotraža 2008 (43°42'17"N, 20°13'34"E), Kotraža 2010 (43°41'49"N, 20°13'9"E), Kupusina 2008 (45°44'24"N, 19°0'6"E), Kupusina 2009 (45°43'49"N, 18°59'58"E), Stanišić 2008 (45°56'48"N, 19°10'51"E), Stanišić 2009 (45°57'7"N, 19°9'10"E), Stanišić 2010 (45°57'46"N, 19°11'6"E), Prijevor (43°54'49"N, 20°16'2"E), Zablaće 2 2009 (43°50'51"N, 20°26'51"E), Zablaće 1 2010 (43°50'27"N, 20°27'25"E).
Figure 2 from: Petrović-Obradović O (2013) Monitoring of aphid flight activities in seed potato crops in Serbia. ZooKeys 319: 333-346. https://doi.org/10.3897/zookeys.319.4315
Figure 2 - Maximum of Shannon–Weaver index per locality (number in brackets - number of weeks of monitoring aphid flight activities, number without brackets - week with maximum value of Shannon–Weaver index).
Figure 8 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 8 - Anthropogenic disturbance within Ankarafa Forest: A Area of savannah, dividing Ankarafa Forest into many smaller fragments (14 December 2011; 14°22.77'S, 47°45.58'E) B Recent forest clearance (11 November 2011; 14°23.09'S, 47°44.92'E) C A fire lit to clear forest for agriculture (16 November 2011; 14°23.20'S, 47°44.80'E) D a Tavy field with intact forest in the background and the river acting as the boundary line (30 October 2011; 14°22.82'S, 47°45.28'E).
Figure 7 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 7 - Habitat of Boophis ankarafensis sp. n. in Ankarafa Forest. A 21 November 2011; 14°23.39'S, 47°46.37'E; B 3 January 2012; 14°22.83'S, 47°45.57'E.
Figure 6 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 6 - Representative call of Boophis ankarafensis sp. n. and comparative call of Boophis bottae from Betampona (Rosa et al. 2011, track #08): A Boophis ankarafensis sp. n. sonagrams (top) and oscillograms (bottom) referring to a section of trill note (type 1) constructed of alternating broad- and narrow-band pulses and click notes (type 2) (recorded at 25.2 °C, 13 October 2011) B Boophis bottae oscillograms of types 1 and 2 notes (recorded at 23.0 °C, 17 November 2007). Spectrogram parameters: FFT length 512, Hamming window.
Figure 5 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 5 - Nocturnal variation in calling activity of Boophis ankarafensis sp. n.; activity shown as a proportion of total calling activity and time shown as a proportion of night length from dusk [0%] until dawn [100%].
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.