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.
28
datasets available to search
ShareScore release 0.9.0
Dataset results
28 results for “larks”
Data associated with Lark et al. 2020: U.S. cropland conversion (2008-16)
<p>Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found <strong><a href="https://zenodo.org/record/3905556#.XvLXQ21Kipo">here</a></strong>.</p> <ul> <li>Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. <em>Nature Communications </em>(In review)</li> </ul> <p>Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:</p> <ul> <li><em><strong>mtr</strong></em> = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period: <ol> <li>"<em>stable non-cropland</em>" -- areas of consistent non-cropland throughout the duration of the study period.</li> <li>"<em>stable cropland</em>" -- areas of consistent cropland throughout the duration of the study period.</li> <li>"<em>cropland expansion</em>" -- areas converted to crop production between 2008 and 2016.</li> <li>"<em>cropland abandonment</em>" -- areas converted away from crop production between 2008 and 2016.</li> <li>"<em>intermittent cropland/confusion</em>" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion. </li> </ol> </li> <li><em><strong>ytc</strong></em> = "year to cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.</li> <li><em><strong>yfc</strong></em> = "year from cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season. </li> <li><em><strong>bfc</strong></em> = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fc</strong></em> = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>bfnc</strong></em> = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fnc</strong></em> = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> </ul> <p>Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (<em>mtr</em> = 3) and those of stable croplands (<em>mtr </em>= 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).</p> <ul> <li><strong>corn_relDiff_local.tif </strong>= local yield differential (%) of corn grain.</li> <li><strong>corn_relDiff_national.tif</strong> = national yield differential (%) of corn grain.</li> <li><strong>soy_relDiff_local.tif</strong> = local yield differential (%) of soybeans.</li> <li><strong>soy_relDiff_national.tif</strong> = national yield differential (%) of soybeans.</li> <li><strong>wheat_relDiff_local.tif</strong> = local yield differential (%) of wheat.</li> <li><strong>wheat_relDiff_national.tif</strong> = national yield differential (%) of wheat.</li> </ul>
Data from: Neo-sex chromosomes and demography shape genetic diversity in the critically endangered Raso lark
Generally small effective population sizes expose island species to inbreeding and loss of genetic variation. The Raso lark has been restricted to a single islet for ~500 years, with a population size of a few hundred. To investigate the factors shaping genetic diversity in the species, we assembled a reference genome for the related Eurasian skylark and then assessed genomic diversity and demographic history using RAD-seq data (26 Raso lark samples and 52 samples from its two most closely related mainland species). Genetic diversity in the Raso lark is lower than in its mainland relatives, but is nonetheless considerably higher than anticipated given its recent population size. This is partly explained by an unusual and dramatic effect of enlarged neo-sex chromosomes, which preserve high heterozygosity across 13% of the genome in females, and account for half of the overall genetic diversity in the population. In addition, by reconstructing past demography we find that genetic signatures of the recent population contraction are overshadowed by an ancient expansion and persistence of a very large population until the human settlement of Cape Verde. Nevertheless, relatedness analyses suggest that the population is at risk of inbreeding depression. Our findings are particularly important in that they reveal the hidden effects of genome architecture in shaping diversity estimates, and hence demonstrate the value of a reference genome and population genomic analyses over conventional metrics to study diversity in non-model and endangered species.
Molecular species delimitation of larks (Aves: Alaudidae), and integrative taxonomy of the genus Calandrella, with the description of a range-restricted African relic taxon
<p>This deposition contains the phylogenetic and species delimitation data for the manuscript "Molecular species delimitation of larks (Aves: Alaudidae), and integrative taxonomy of the genus <em>Calandrella</em>, with the description of a range-restricted African relic taxon" by Stervander <em>et al</em>. </p> <p>For details of samples/sequences/leaves, please refer to Appendix A of the above manuscript. </p> <p><strong>Phylogenetic analyses</strong></p> <ol> <li>Fasta sequence alignment of cytochrome b for the lark family and outgroups: Alaudidae_cytb_extended_200316.fa</li> <li>BEAST v. 2.6.1 input file: Alaudidae_cytb_HKYGI_BDrelLN_modOp2003_20M1K.xml</li> <li>BEAST v. 2.6.1 output log file: Alaudidae_cytb_HKYGI_BDrelLN_modOp_20M1K.log</li> <li>BEAST v. 2.6.1 output (raw) trees file: Alaudidae_cytb_HKYGI_BDrelLN_modOp_20M1K.trees</li> <li>TreeAnnotator maximum credibility clade tree based on BEAST v. 2.6.1 output, newick format: Alaudidae_cytb_HKYGI_BDrelLN_modOp_20M1K_c40Mbi5.mccmed.nwk</li> </ol> <p><strong>Species delimitation</strong></p> <ol> <li>Input tree, manipulated to remove negative branch lengths (replaced by 0) and tips/leafs that are single representatives of a species, based on current taxonomy (IOC v. 10.2), newick format: Alaudidae_cytb_HKYGI_BDrelLN_c40Mbi5_mccmed_nonNeg_multiSeq_ingroup_remDuplicate.nwk</li> <li>mPTP text output of the multi-rate species delimitation, containing command for run and species delimitation results: Alaudidae200316_cytb_HKYGI_BDrelLN_c40Mbi5_remDuplCaboweni201007_mccmed_delim_varRate_10M_minbrAUTO.1602073064.txt</li> <li>mPTP likelihood log of the multi-rate species delimitation: Alaudidae200316_cytb_HKYGI_BDrelLN_c40Mbi5_remDuplCaboweni201007_mccmed_delim_varRate_10M_minbrAUTO.out.txt</li> <li>mPTP output tree in SVG format, with support values for species delimitation: Alaudidae200316_cytb_HKYGI_BDrelLN_c40Mbi5_remDuplCaboweni201007_mccmed_delim_varRate_10M_minbrAUTO.1602073064.combined.svg</li> </ol>
Dataset: Landmark Bancorp, Inc. (LARK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2 in A review of the status, distribution and ecology of Friedmann's Lark Mirafra pulpa, including its habitat associations
Figure 2. Spatial distribution of records of Friedmann's Lark Mirafra pulpa records in relation to the cindercone formations of the Dukana / Huri Hills in Kenya (KE) and Ethiopia (ET), and the Nyambeni and Chyulu Hills in Kenya. Records extend to Tanzania (TZ).
Figure 1 in A review of the status, distribution and ecology of Friedmann's Lark Mirafra pulpa, including its habitat associations
Figure 1(a) Monthly (1912–2018) and (b) annual (since 1972) summaries of Friedmann's Lark Mirafra pulpa records.
Figure 3 in A review of the status, distribution and ecology of Friedmann's Lark Mirafra pulpa, including its habitat associations
Figure 3. Satellite imagery (© Google Earth) showing the denser grassland and poorer drainage (evidenced by abundant water pans) that characterise white ash-based soils (right side) in comparison to red soils (left side) in the Kilaguni Lodge area of Tsavo West National Park (Kenya).
Data from: Genetic diversity, demographic history and neo-sex chromosomes in the Critically Endangered Raso lark
Open the record for dataset details and reuse information.
Data and code for: Plumage balances camouflage and thermoregulation in Horned Larks (Eremophila alpestris)
Open the record for dataset details and reuse information.
Data for: Mobbing for matings: dynamics, plumage correlates, and fitness impacts of conspicuous group extra-pair behaviors in the lark bunting
Open the record for dataset details and reuse information.
Data from: Wind farms affect the occurrence, abundance and population trends of small passerine birds: the case of the Dupont's lark
1.The assessment of the effects of wind farms on bird populations is commonly based on collision fatality records. This could undervalue the effect of wind farms on small-sized birds. We evaluate the effect of wind turbines on occurrence, abundance and population trends of a threatened small passerine species, the Dupont's lark Chersophilus duponti. To our knowledge, this is one of the first studies addressing the effect of wind farms on population trends using time series data from multiple wind farms. 2.We estimated population trends by fitting a switching linear trend model with the software TRIM (Trend & Indices for Monitoring data). We used multiannual data surveys of five populations in the presence of wind farms and nine in their absence (2008–2016 period). Furthermore, we fitted a logistic and a negative binomial regression model to test the effect of wind farm proximity on species occurrence and abundance in 2016, respectively. We incorporated local connectivity and habitat availability estimates in both models as predictors. 3.Results showed a negative trend overall, but that was significantly more regressive in the presence of wind farms: 21.0% versus 5.8% average annual decline in the absence of wind farms. 4.Dupont's lark occurrence and abundance in 2016 were negatively affected by measures of population isolation and positively affected by the distance to wind farms. 5.These results highlight the negative effect of isolation and wind farm proximity on Dupont's lark population parameters. Taking into account the metapopulation structure exhibited by the species in the study area, this work established a 4.5 km threshold distance from wind farms, beyond which Dupont's lark populations should be unaffected. 6.Synthesis and applications. This work highlights the negative impact of wind farms on small-sized birds and provides a 4.5 km threshold distance that should be taken into account in the design of future wind energy projects. Moreover, we suggest an analytical approach based on population trends, species abundance and occurrence variation in relation to wind farms, useful for the assessment of wind farm impacts on small-sized birds.
Figure 7 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 7. Plots of the first two components of a linear discriminant analysis of songs among the six primary clades in the Corypha africana complex, excluding the type 2 song of C. a. athi (A) and songs among Corypha hypermetra (C. h. hypermetra and C. h. gallarum), C. h. kidepoensis, C. somalica and the 'type 2' song of C. a. athi (B).
Figure 8 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 8. Summary of evidence. A, tree with support for different clades based on different datasets, labelled with the proposed new taxonomy. Symbols are explained at the top: black symbols = strong support; grey symbols = weak support; unfilled symbols = no support; white triangle with question mark = lack of data. *Restricted datasets were analysed for Corypha kidepoensis: only Cytb in the multilocus analyses, and a separate single nucleotide polymorphism (SNP)-based analysis not included in the principal component analysis (see main text). #Taxon featured in photograph. B, pairwise differentiation between all taxa, based on mitochondrial DNA, genome-wide SNPs, plumage, morphometrics, song and other behaviour. The full key and an example are at the top of the panel. For photograph credits, see Figure 4.
Figure 4 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 4. Principal component analysis (PCA) based on seven morphometric variables: the lengths of wing, bill, tarsus, tail, hind claw and outermost primary, and the distance from the outermost primary to the wing tip. Ellipses indicate the 68% confidence interval (±1 SD) for each clade. Loadings for each trait, drawn with maps and their relative contributions across all principal components (PCs), can be viewed in the Supporting Information (Fig. S9). Photographs are by Nik Borrow (II, kidepoensis Kidepo National Park, Uganda, May; III, somalica Tuuyo Plains, Somaliland, September; VII, nyikae Nyika National Park, Malawi, December), Maans Booysen, Birding Weto, CC BY-SA 4.0 https:// creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons (VI, malbranti Longa, Angola), Paul F. Donald (Va, pallida, Caprivi Strip, Namibia, November), Emmanuel Barde Elisha (VIII, bamendae Mambilla Plateau, Nigeria), Stratton Hatfield (I, hypermetra Tsavo East National Park, Kenya, March), Peter Steward (IV, athi Nairobi National Park, Kenya, March) and The Trustees of The Natural History Museum, London (II, sharpii NHMUK, identity and collection information lacking). The taxa with photographs are also indicated by '#', and '*' for sharpii.
Figure 5 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 5. Sonograms of songs of taxa treated as subspecies of Corypha africana, C. hypermetra and C. somalica. The strophes are numbered consecutively for precise reference in the text. Recordings: (1) Foumban-Tibati, Cameroon, BL 022A-W1CDR0000054 BD19 (Claude Chappuis); (2) Mambilla Plateau, Nigeria, ML274689 (Emmanuel Elisha); (3) Mambilla Plateau, Nigeria, ML274691 (Emmanuel Elisha); (4) Mambilla Plateau, Nigeria (Emmanuel Elisha); (5) Nyika National Park, Malawi, ML278996 (Peter Boesman); (6) Nyika National Park, Malawi, ML279004 (Peter Boesman); (7) Nyika National Park, Malawi, ML279005 (Peter Boesman); (8) Kipengere Plateau, Tanzania, ML562576011 (David Moyer); (9, 10) Kipengere Plateau, Tanzania, ML562576201 (David Moyer); (11) Minyanya plain, north-west Zambia, ML279942 = XC509007 (Peter Boesman); (12) Minyanya plain, north-west Zambia, ML279943 = XC509008 (Peter Boesman); (13) Minyanya plain, north-west Zambia, ML279957 = XC509010 (Peter Boesman); (14) Lékoni, Gabon, ML274685 (Nik Borrow); (15) south-east Gabon, Chappuis CD8 BD91 (P. Christy); (16) Lékoni, Gabon (Michael Mills); (17) Polokwane, Limpopo, South Africa, ML274677 (Per Alström); (18) Sandveld, South Africa, XC28667 (Patrik Åberg); (19) Free State, South Africa, XC536937 (Dawie de Swardt); (20) Polokwane, South Africa, ML274681 (Per Alström); (21) North West Province, South Africa, XC516080 (Frank Lambert); (22) Polokwane, Limpopo, South Africa, XC516146 (Frank Lambert); (23) Lusaka, Zambia, BL WS3371 C15 (Robert Stjernstedt); (24) Lusaka, Zambia, BL WS3371 C15 (Robert Stjernstedt); (25) Lusaka, Zambia, BL WS3371 C10 (Robert Stjernstedt); (26) Etosha National Park, Namibia, ML42945 (Linda Macaulay); (27) Etosha National Park, Namibia, ML61179 (Linda Macaulay); (28) Namutoni, Namibia, XC58667 (Charles Hesse); (29) Maasai Mara, Kenya, ML274675 (Stratton Hatfield); (30) Maasai Mara, Kenya, ML274674 (Stratton Hatfield); (31) Mara Triangle, Kenya, XC200107 (Rory Nefdt); (32) Entebbe, Uganda, ML8031 (Myles E. W. North); (33) Uganda, ML23453 (G. Stuart Keith); (34) Semliki Flats, Uganda, ML23454 (G. Stuart Keith); (35) Lake Nakuru, Rift valley, Kenya, ML8032 (Myles E. W. North); (36) Nairobi, Kenya, ML8034 (Myles E. W. North); (37) Nairobi, Kenya, ML274661 (Per Alström); (38) Lake Nakuru, Rift
Figure 3. Multilocus tree for the Corypha africana–C. sharpii–C. hypermetra–C. somalica–C in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 3. Multilocus tree for the Corypha africana–C. sharpii–C. hypermetra–C. somalica–C. ashi complex based on Bayesian analysis (BEAST) of concatenated mitochondrial and nuclear loci (5,285 bp). Posterior probabilities (PPs) are indicated at the nodes, with an asterisk indicating a PP of 1.00. Clades that are discussed in the text are labelled I–VIII and with the name of the taxon that has priority in the respective clade. The names of C. africana s.l. are in black font; the other species are in other colours (the six outgroup species are all in pale blue). The three taxa that are not present in any other analyses are indicated by a red '§'. Topological incongruence compared with other phylogenetic trees is indicated by a red '#'. The blue lines indicate the divergence times of two of the outgroup species pairs.
Figure 2 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 2. Phylogenies of the Corypha africana–C. sharpii–C. hypermetra–C. somalica–C. ashi complex, with Corypha fasciolata as the outgroup (the more distant outgroup species Amirafra rufocinnamomea has been removed) based on the multi-species coalescent, produced within a Bayesian framework in SNAPP on 16,412 single nucleotide polymorphisms (SNPs) and single individuals per taxon (A) and with ASTRAL, summarising taxon quartets in maximum likelihood (ML) analyses of 32,000 SNPs across 626 unlinked windows for all samples (B). Node support values are posterior probabilities (PPs) for SNAPP and local PPs for ASTRAL, with an asterisk indicating a PP of 1.00. Clades that are discussed in the text are labelled I–VIII and with the name of the taxon that has priority in the respective clade. The names of C. africana s.l. are in black font; the other species are in other colours. Topological incongruence between these two trees, the Snapper species tree (Supporting Information, Fig. S3), the ML IQ-TREE tree (Supporting Information, Fig. S5) and/or the Bayesian multilocus tree (Fig. 3) are indicated by a red '#'.
Figure 6. A in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 6. A, mean and SD for eight song variables for the five primary clades in the Corypha africana complex (first song type, corresponding to Fig. 5: strophes 1–40). B, mean and SD for six song variables for type 2 song of C. a. athi, and song of Corypha hypermetra and C. somalica (corresponding to Fig. 5: strophes 41–57).
Figure 1 in Integrative taxonomy reveals unrecognised species diversity in African Corypha larks (Aves: Alaudidae)
Figure 1. Distribution of the Corypha africana–C. sharpii–C. hypermetra–C. somalica–C. ashi complex based on BirdLife International and Handbook of the Birds of the World (2020) and Kennedy and Finch (in prep.), with type localities indicated by asterisks. Note that information on distribution should be regarded with caution, because further research is required for precision and accuracy. The distributions of specific taxa are listed in Table 2, but their boundaries (particularly within and between the two large continuous ranges in southern/southwestern Africa and East Africa) are unclear and in need of updating. For example, there have been records further east in southern Tanzania and northern Mozambique (Baker and Baker 2014; Supporting Information, Fig. S1B), indicated on the map by a question mark. Some additional information on distribution can be gleaned from Supporting Information, Figure S1, which shows localities for birds that have been sampled genetically or whose song has been recorded.
Data from: Multiple instances of paraphyletic species and cryptic taxa revealed by mitochondrial and nuclear RAD data for Calandrella larks (Aves: Alaudidae)
Open the record for dataset details and reuse information.
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.