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2,967 results for “secondary”
Data from the NASCENT campaign used in the publications: "Conditions favorable for secondary ice production in Arctic mixed-phase clouds" and "Understanding the history of two complex ice crystal habits deduced from a holographic imager"
<p>This repository contains the data from the Ny‐Ålesund AeroSol Cloud ExperimeNT campaign (NASCENT). This data were used to produce the figures in the two papers:</p> <p>(1) Pasquier, J. T., Henneberger, J., Ramelli, F., Korolev, A.,Wieder, J., Lauber, A., Li, G., David, R. O., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Understanding the history of two complex ice crystal habits deduced from a holographic imager, Geophys. Res. Lett., accepted, 2022</p> <p> </p> <p>(2) Pasquier J. T., Henneberger J., Ramelli F., Lauber A., David O. D., Wieder J., Carlsen T., Gierens R., Maturilli M., and Lohmann U.:Conditions favorable for secondary ice production in Arctic mixed-phase clouds, ACP, accepted.</p> <p>More information can be found in the README files.</p> <p> </p> <p>The scripts to reproduced the Figures are available on Zenodo</p> <p>(1) https://doi.org/10.5281/zenodo.7402296</p> <p>(2) https://doi.org/10.5281/zenodo.7407107</p>
Short-term effects of the control of an invasive plant Asclepias syriaca: secondary invasion of other neophytes instead of the recovery of native species
<p>Data sets to article: "Short-term effects of the control of an invasive plant <em>Asclepias syriaca</em>: secondary invasion of other neophytes instead of the recovery of native species".</p> <p>We studied the impact of <em>Asclepias syriaca</em>, a non-native herb species, on basic soil attributes and vegetation composition in sandy grasslands and the effect of mechanical control of this species. The <em>Asclepias </em>invasion changed the vegetation composition, but not the studied soil attributes. The shot-term cutting suppressed <em>Asclepias</em>, but instead of the recovery of native species, secondary invasion by other alien species occurred.</p>
Data and code from paper: The carbon sink of secondary and degraded humid tropical forests
<p>This repository contains the data and code produced for the following paper:</p> <p><strong>Title: </strong>The carbon sink of recovering secondary and degraded humid tropical forests</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>Please note:</strong></p> <ul> <li> throughout repository where files include reference to: <...<strong>congo_basin</strong>...> this refers to the <strong>Central Africa </strong>region as it is termed in the main paper.</li> <li>the <strong>code</strong> <strong>has not been amended</strong> for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. </li> </ul> <p>The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in <strong>R (v3.6) for which the code and output datasets are available here.</strong></p> <p>To access the code used in <strong>Google Earth Engine</strong> that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4 </p> <p>This repository contains the following zipped folders:</p> <ul> <li><strong>data_folder</strong>: this folder contains further folders with all the data produced for this paper.</li> </ul> <ol> <li>Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "<strong>regrowthModel_..RDS</strong>. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.</li> <li>Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "<strong>regrowthModel_..RDS</strong>) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. </li> <li>Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.</li> <li>Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.</li> <li>Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.</li> <li>Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. </li> </ol> <ul> <li><strong>code_folder.zip</strong>: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. <ul> <li>this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. </li> </ul> </li> </ul> <p><strong>Data and Code usage:</strong> When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository. </p> <p>Further source data in .xlsx format were also submitted with the main manuscript.</p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Secondary ion mass spectrometry, a powerful tool for revealing ink formulations and animal skins in medieval manuscripts
<p>Book production by medieval scriptoria has gained growing interest in recent studies. In this context, identifying ink compositions and parchment animal species from illuminated manuscripts is of great importance. Here, we introduce time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a non-invasive tool to identify both inks and animal skins in manuscripts, at the same time. For this purpose, both positive and negative ion spectra in inked and non-inked areas were recorded. Chemical compositions of pigments (decoration) or black inks (text) were determined by searching for characteristic ion mass peaks. Animal skins were identified by data processing of raw ToF-SIMS spectra using principal component analysis (PCA). In illuminated manuscripts from the fifteenth to sixteenth century, malachite (green), azurite (blue), cinnabar (red) inorganic pigments, as well as iron-gall black ink, were identified. Carbon black and indigo (blue) organic pigments were also identified. Animal skins were identified in modern parchments of known animal species by a two-step PCA procedure. We believe the proposed method will find extensive application in material studies of medieval manuscripts, as it is non-invasive, highly sensitive and able to identify both inks and animal skins at the same time, even from traces of pigments and tiny scanned areas.</p>
Data on learning about green space management by upper secondary school students
<p>A workshop survey -dataset detailing how upper secondary school students learn about green space management.</p>
Dataset for "Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>A qualitative dataset for the article: Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources</p> <p>We collected this data in Phase 2 of the work described in the article, to inform development of educational resources (<em>Be Smart About Your Health</em>) to support teaching critical thinking about health claims and making informed health choices for use in secondary schools, based on a set of Informed Health Choices Key Concepts. </p> <p>Data collection methods: individual and group interviews, observation of classroom pilots, in Kenya, Rwanda, and Uganda, and via email from an international advisory group. Timeframe for data collection and analysis: 2020-2022</p> <p>This dataset is a part of the research project: <em>Enabling sustainable public engagement in improving health and health equity, </em>2019-2024. Funded by GLOBVAC programme, Research Council of Norway. </p> <p> </p>
FIG. 7 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 7. — Distribution of Cetrelia cetrarioides (Delise) W.L. Culb.& C.F.Culb. in Hungary after revision.
FIG. 11 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 11. — Distribution of Cetrelia olivetorum (Nyl.) W.L. Culb. & C.F.Culb.in Hungary after revision.
FIG. 4 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 4. — Microcrystal tests of A, imbricaric; B, perlatolic; C, olivetoric acids and D, atranorin in GE solvent (glycerine – acetic acid, 3:1 v/v). Scale bars: 50 µm.
FIG. 6 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 6. — Distribution of Cetrelia cetrarioides (Delise) W.L. Culb. & C.F. Culb.in Hungary before revision.
FIG. 10 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 10. — Distribution Cetrelia olivetorum (Nyl.) W.L. Culb.& C.F. Culb.in Hungary before revision.
FIG. 2. — A in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 2. — A detail of the chromatographic plate HPTLC nr 74/2014 developed in solvent system C presenting all species under UV 254 nm (A), and sprayed with water (B). Specimens in positions A10-A17 are A10: C. olivetorum (Nyl.) W.L. Culb. & C.F. Culb. (BP 21529), A11: C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb. (BP 85318), A12: C. olivetorum (BP 84893), A13: C. cetrarioides (Delise) W.L. Culb. & C.F. Culb. (BP 21538), A14: C. olivetorum (BP 22787), A15: C. monachorum (BP 45013), A16: C. olivetorum (BP 21508), A17: C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb. (BP 93416). Abbreviations of LSMs are according to Table 2.
FIG. 5 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 5. — Thalline lobes with marginal soralia of A, Cetrelia cetrarioides (Delise) W.L. Culb. & C.F. Culb.; B, C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb.; C, C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb.; and D, C. olivetorum (Nyl.) W.L. Culb. & C.F. Culb. Scale bars: 1 mm.
FIG. 1 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 1. — Differential morphological and chemical features in Cetrelia W.L. Culb. & C.F. Culb. species: A, large, not raised pseudocyphellae on upper cortex of C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb.; B, small, raised pseudocyphellae on upper cortex of C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb.; C, brown lower cortex of C. cetrarioides (Delise) W.L. Culb. & C.F. Culb. without rhizines; D, C+ reaction by NaOCl on marginal soralia of Cetrelia olivetorum (Nyl.) W.L. Culb. & C.F. Culb. Scale bars: 500 µm.
FIG. 13 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 13. — The number of Cetrelia W.L. Culb. & C.F. Culb. species after revision in various parts of Hungary.
FIG. 3. — A in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 3. — A detail of the chromatographic plate TLC nr 1903 developed in solvent system C presenting C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb. specimens under UV 254 nm (A), UV 366 nm (B) and sprayed with anisaldehyde/sulphuric acid (C). Specimens in positions 3-9 are: 3, from Page County, Virginia, United States (BP 75822); 4, from the Bükk Mts, Hungary (BP 71276); 5, from the Zemplén Mts, Hungary (BP 49905); 6, from Pocahontas County, West Virginia, United States (BP 91365); 7, from Ukraine (BP 44999); 8, from Romania (BP 85320); 9, from Poland (BP 21508). Abbreviations of LSMs are according to Table 2.
The use of lexicographic resources in Croatian primary and secondary education - Survey Data
<p>The dataset contains the data collected in the survey on the use of dictionaries and other lexicographic resources in Croatian primary and secondary education, which was conducted from 1 February to 17 February 2023.</p>
FIG. 15 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 15. — Cladonia novochlorophaea (Sipman) Brodo & Ahti: A, habit (BP[BP 9314]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: H, homosekiaic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 8 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 8. — The mean diameter of soredia (µm) measured on podetia (n = 10). Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 5 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 5. — Height of cup (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
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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.