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2,096 results for “Tracts”
Tree Ring Data from the Harvard Tract in Pisgah State Forest NH 1675-2015
Is it possible to reconstruct aboveground carbon/biomass from tree rings? If so, how far back in time can researchers go when using tree-ring data in the reconstruction of past biomass? Answers to these questions will have a significant impact on our understanding of dynamics in the terrestrial carbon sink. Long tree-ring records of biomass can reveal intra-annual to annual to multidecadal variations that cannot be resolved by forest census data that is not conducted at annual time steps. Additionally, while these dynamics might be resolved using remote sensing, most remotely-sensed products are only two decades or less in length. By having long records of carbon biomass, we can then identify not only the dominant drivers of biomass, but how the importance of these drivers might change during different eras as environmental factors change (e.g., climate, air pollution, disturbance). To test these and other questions, we collected tree-ring records from two 30m radius plots set within Harvard’s Pisgah Tract in Pisgah State Forest, New Hampshire. We can convert these data to biomass using allometric equations and compare tree-ring inferred aboveground biomass to the census data going back in time. Famously, 80% of this tract was knocked down in September 1938 by a strong hurricane. The forest has sine regrown and the plots we installed can be used to understand forest recovery and carbon dynamics in a heavily disturbance forest. Given that this was a known/decently documented event, these data were used by Trotsiuk et al. (2018) to test various growth release methods as applied to tree-ring data. Trotsiuk, V., Pederson, N., Druckenbrod, D. L., Orwig, D. A., Bishop, D. A., Barker Plotkin, A., Fraver, S., Martin-Benito, D. 2018. Testing the efficacy of tree-ring methods for detecting past disturbances. Forest Ecology and Management 425: 59-67.
Longitudinal Streamflow in Headwater Streams on Prospect Hill Tract at Harvard Forest 2003
We have initiated long-term monitoring of streamflow in headwater streams on the Prospect Hill Tract at the Harvard Forest. In addition, we have periodically recorded summer flow conditions longitudinally along the length of headwaters within Harvard Forest to obtain information about spatial heterogeneity of flow and of availability of aquatic habitat within these headwaters. In summer of 2003, we recorded streamflow each week at 20-m intervals along the length of two tributaries of Nelson Brook, Tributary A, from Route 32 to its outlet from the Black Gum Swamp, and Tributary B, from its junction with Tributary A to its origin in wetlands north of Prospect Hill Road.
Land Conservation and Human Demographics by Census Tract in New England 2014-2018
This dataset summarizes land protection, conservation prioritization layer scores, and human demographics within New England communities, defined as census tracts. This dataset was created to identify disparities in land protection according to metrics of social marginalization and assess how incorporating environmental justice criteria into land conservation prioritization systems might change conservation priorities.
Historical GIS Data for Prospect Hill Tract at Harvard Forest 1733-1986
This dataset contains elevation, 1986 forest type, land-use history, and soils maps for the Prospect Hill Tract, digitized from paper maps in the Harvard Forest Archives. File format = Idrisi 4.1 binary. Resolution = 10m x 10m. Coordinates = UTM zone 18. Datum = 1927 North American. This dataset has been replaced with a new vector series for the entire Harvard Forest (see HF110).
The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma
<p>The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry<sup>TM</sup> (IMC<sup>TM</sup>) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.</p>
Plot-based vegetation data for a large tract of old--growth hemlock-northern hardwood forest, Marquette Co., Michigan: 1988
In 1987-88 members of the Burton V. Barnes lab at University of Michigan conducted a landscape inventory of portions of the Huron Mountain Club lands (primarily, the self-declared 'Reserved Area') in Powell Township, northern Marquette County, MI. The data-set deposited here, collect under direction of Philip E. Stuart (then a graduate student in the lab) focuses on the ca. 1200 ha of old-growth, mesic hemlock-northern hardwood forests within the larger property. 313 plots (450 m^2) were established at nodes of an approximately 10 chain (~192 m) grid that fell within these forest types. The data-set includes canopy tree measurements, ground-layer cover estimates (for a subpplot), and a number of soil and topographic variables (measured directly and derived). A description of the study and results is published in Simpson et al. 1990. Occasional Papers of the Huron Mountain Wildlife Foundation Number 4, with associated maps.
F-TRACT atlas
<p>F-TRACT atlas release - December 2021<br> ======================================</p> <p>The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas.</p> <p>- f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100").</p> <p>- Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.<br> </p>
High quality white matter reference tracts
<p><strong>Overview</strong></p> <p>This dataset contains segmentations of 72 white matter tracts obtained from 105 subjects included in the Human Connectome Project (HCP) young adult dataset (https://www.humanconnectome.org/study/hcp-young-adult). The folder names correspond to the ID of the HCP subjects. This dataset only contains the tracts. It does not contain the original DWI data. This has to be downloaded from the HCP website (it is free, but you have to register to get access).</p> <p>The data is part of the following publication: <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">Wasserthal et al., TractSeg - Fast and accurate white matter bundle segmentation. NeuroImage (2018)</a>. If you use the data please cite the paper.</p> <p> </p> <p><strong>Details for generating corresponding whole brain tractograms</strong></p> <p>The tracts were extracted semi-automatically from whole-brain tractograms. For a detailed description of the tract segmentation process please refer to the paper. The following MRtrix (http://www.mrtrix.org/) commands were used to obtain the whole-brain tractograms:</p> <p>5ttgen fsl T1w_acpc_dc_restore_brain.nii.gz 5TT.mif -premasked<br> dwi2response msmt_5tt Diffusion.nii.gz 5TT.mif RF_WM.txt RF_GM.txt RF_CSF.txt -voxels RF_voxels.mif -fslgrad Diffusion.bvecs Diffusion.bvals<br> dwi2fod msmt_csd Diffusion.nii.gz RF_WM.txt WM_FODs.mif RF_GM.txt GM.mif RF_CSF.txt CSF.mif -mask nodif_brain_mask.nii.gz -fslgrad Diffusion.bvecs Diffusion.bvals<br> tckgen -algorithm iFOD2 WM_FODs.mif output.tck -act 5TT.mif -backtrack -crop_at_gmwmi -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0</p> <p>For "CA", "IFO_left", "IFO_right", "UF_left", "UF_right" we used tracking without anatomical constraints:</p> <p>tckgen -algorithm iFOD2 WM_FODs.mif output.tck -seed_image nodif_brain_mask.nii.gz -maxlength 250 -minlength 40 -number 10M -cutoff 0.06 -maxnum 0</p> <p>Due to their enormous size, the whole brain tractograms corresponding to the segmented tracts are not included this dataset. Please contact the author of the paper if you are interested in these tractograms.</p> <p> </p> <p><strong>Included tracts</strong></p> <p>1: AF_left (Arcuate fascicle)<br> 2: AF_right<br> 3: ATR_left (Anterior Thalamic Radiation)<br> 4: ATR_right<br> 5: CA (Commissure Anterior)<br> 6: CC_1 (Rostrum)<br> 7: CC_2 (Genu)<br> 8: CC_3 (Rostral body (Premotor))<br> 9: CC_4 (Anterior midbody (Primary Motor))<br> 10: CC_5 (Posterior midbody (Primary Somatosensory))<br> 11: CC_6 (Isthmus)<br> 12: CC_7 (Splenium)<br> 13: CG_left (Cingulum left)<br> 14: CG_right <br> 15: CST_left (Corticospinal tract<br> 16: CST_right <br> 17: MLF_left (Middle longitudinal fascicle)<br> 18: MLF_right<br> 19: FPT_left (Fronto-pontine tract)<br> 20: FPT_right <br> 21: FX_left (Fornix)<br> 22: FX_right<br> 23: ICP_left (Inferior cerebellar peduncle)<br> 24: ICP_right <br> 25: IFO_left (Inferior occipito-frontal fascicle) <br> 26: IFO_right<br> 27: ILF_left (Inferior longitudinal fascicle) <br> 28: ILF_right <br> 29: MCP (Middle cerebellar peduncle)<br> 30: OR_left (Optic radiation) <br> 31: OR_right<br> 32: POPT_left (Parieto‐occipital pontine)<br> 33: POPT_right <br> 34: SCP_left (Superior cerebellar peduncle)<br> 35: SCP_right <br> 36: SLF_I_left (Superior longitudinal fascicle I)<br> 37: SLF_I_right <br> 38: SLF_II_left (Superior longitudinal fascicle II)<br> 39: SLF_II_right<br> 40: SLF_III_left (Superior longitudinal fascicle III)<br> 41: SLF_III_right <br> 42: STR_left (Superior Thalamic Radiation)<br> 43: STR_right <br> 44: UF_left (Uncinate fascicle) <br> 45: UF_right <br> 46: CC (Corpus Callosum - all)<br> 47: T_PREF_left (Thalamo-prefrontal)<br> 48: T_PREF_right <br> 49: T_PREM_left (Thalamo-premotor)<br> 50: T_PREM_right <br> 51: T_PREC_left (Thalamo-precentral)<br> 52: T_PREC_right <br> 53: T_POSTC_left (Thalamo-postcentral)<br> 54: T_POSTC_right <br> 55: T_PAR_left (Thalamo-parietal)<br> 56: T_PAR_right <br> 57: T_OCC_left (Thalamo-occipital)<br> 58: T_OCC_right <br> 59: ST_FO_left (Striato-fronto-orbital)<br> 60: ST_FO_right <br> 61: ST_PREF_left (Striato-prefrontal)<br> 62: ST_PREF_right <br> 63: ST_PREM_left (Striato-premotor)<br> 64: ST_PREM_right <br> 65: ST_PREC_left (Striato-precentral)<br> 66: ST_PREC_right <br> 67: ST_POSTC_left (Striato-postcentral)<br> 68: ST_POSTC_right<br> 69: ST_PAR_left (Striato-parietal)<br> 70: ST_PAR_right <br> 71: ST_OCC_left (Striato-occipital)<br> 72: ST_OCC_right<br> </p> <p><strong>Cross-validation data splits</strong></p> <p>The following data splits were used for cross-validation in the TractSeg paper:</p> <pre><code class="language-python">fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044'] fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766'] fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056'] fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152'] fold5 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']</code></pre> <p>Hyperparameters were optimized using fold 1-3 for training and fold 4 for validation.</p> <p>The final 5-fold cross-validation (results reported in the TractSeg paper) was done by always training on 3 folds, selecting the best epoch by evaluating on the fourth fold and then reporting the final results (of the model from the best epoch) on the fifth fold.</p> <p>The pretrained TractSeg model which will automatically be used when you download TractSeg was trained on fold1+fold2+fold3.</p> <p>Please use the same data splits to make your work comparable.</p> <p> </p> <p><strong>Data format</strong></p> <p>From version 1.2.0 of this dataset onwards it uses the newest trackvis (trk) standard (using nibabel.streamlines API). Streamlines are saved in native voxel space and when loaded are transformed to coordinate space using the affine stored in the trk file header. In the previous versions of the dataset the older nibabel.trackvis API was used (streamlines are saved in real coordinate space and no affine is applied when loading them).</p>
Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tract
<p>Segmentation of the 13 experts and 11 non-experts for the Tractostorm project. Contains the original dataset each participants received to perform their tasks.</p> <p>The tractography file format is *.trk and the image file format is *.nii.gz</p>
Urban Redevelopment by Census Tract in New York City (2000 -2020)
<p>The annual urban redevelopment map for NYC was produced using the classification method proposed in this experiment to highlight the spatial and temporal distribution of urban reconstructions. The time-series gentrification risk maps illustrate areas that have faced gentrification risk since 2000. The data was aggregated to the Census tract level for displaying a visually friendly result. The raw building-level data is also provided.</p>
Intensive male competition caused severity of trauma in female genital tracts predicts female reproductive success and longevity in strictly monandrous wolf spiders
<p>This is the raw data for the manuscript of Dr. Shichang Zhang from Hubei University entitled: <strong>Intensive male competition caused severity of trauma in female genital tracts predicts female reproductive success and longevity in strictly monandrous wolf spiders. </strong></p>
FIGURE 9 in The alimentary tract of arostrate, dwarFIsh males of the aphid genus Stomaphis (Insecta, Hemiptera)
FIGURE 9 Droplets of fluid excreted by Stomaphis grafFIi males when disturbed within colony (A) and after collection (B).
FIGURE 1 in The alimentary tract of arostrate, dwarFIsh males of the aphid genus Stomaphis (Insecta, Hemiptera)
FIGURE 1 Body of an adult male of S. grafFII: ventral and dorsal (A) view, with aedeagus partly everted; head in microscopic slide (B) with reduced mouthparts and their close-up in macroscopic view (C).
Dataset for: Mechanisms of uropathogenic E. coli mucosal association in the gastrointestinal tract (Science Advances)
<p>This dataset contains the plotted data for Figures 1B, 1B, 1F, Figure 4C, Figures 5C, 5E, 5G, 5H, 5J, 5K, Figure S5B, Figure S6A, S6B, S6C, Figure S7 A, S7C, S7D, and Figure S8C.</p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
FIGURE 3. Female genitalia and reproductive tract. A and C. A in A New Species of the New Zealand Endemic Genus Actenonyx White, 1846 (Coleoptera: Carabidae: Odacanthini) with Notes on Variation, Distribution, and Habitat
FIGURE 3. Female genitalia and reproductive tract. A and C. A. bembidioides White (South Island, Southland, Te Anau Downs, Mistletoe Creek, 216 m); B and D. A. aotearoa sp. nov. (South Island, Fiordlands National Park, east end of Homer Tunnel, 915m); A and B. Tergite IX, dorsal aspect; C and D. Genital capsule and reproductive tract; bc = bursa copulatrix; co = common oviduct; gc1 = gonocoxite 1; gc2 = gonocoxite 2; sbb = spermathecal basal bulb; sg = spermathecal gland; sgd =spermathecal gland duct. Scale lines = 0.5 mm.
Figure 1 in Experimental evidence that the invasive snail Potamopyrgus antipodarum (Gray, 1843) survives passage through the digestive tract of common riverine fish
Figure 1. The Number of ingested P. antipodarum individuals per fish (red bars) and the number of snails that survived passing through the digestive tract (green bars). Values are mean ± standard deviation.
Fig. 2 in Low Level Genetic Diversity of Opalinid Morphotypes from the Digestive Tract of Hoplobatrachus rugulosus (Batrachia, Amphibia) in Thailand
Fig. 2. Scanning electron micrographs of opaline cells fixed in 2.5% glutaraldehyde. All opaline cells are covered with flagella throughout their body. (A) Opalinid cell with somatic ridges caused by the flagellar metachronal beating clearly defined in a spiral arrangement. (B) Fan-shaped opalinid cell with a broad anterior end and tapering posterior. (C) Uniformly elongated opalinid cell with a slightly tapering posterior end. (D) Fan-shaped opalinid cell of a similar morphotype to B but of a smaller size. Scale bar: A, C and D: 50 μm; B: 10 μm.
Fig. 1 in Low Level Genetic Diversity of Opalinid Morphotypes from the Digestive Tract of Hoplobatrachus rugulosus (Batrachia, Amphibia) in Thailand
Fig. 1. Light micrographs of opaline cells from life. (A) The opaline falx (arrow). (B) Multiple nuclei throughout the cell (arrowheads). A bend at the mid-body level is noticeable. (C) Flagella covering the body of the opaline cell (arrow). Several scores of nuclei are clearly visible (small circular clear whitish areas). (D) Prominent ridges signifying the metachronal beating of the flagella cover the opaline body in a random arrangement (arrow). (E) Flagella beating in a metachronal fashion (arrow). Opalinid cell nuclei (arrowheads). Scale bar: A and C: 100 μm; B, D and E: 50 μm.
Figure 3 in Diversity of cellulolytic and xylanolytic fungi associated with the digestive tract of aquatic insect larvae in streams of the Amazon Forest and Cerrado in Brazil
Figure 3. Percentage of fungal isolates from the DT of Stenochironomus (Diptera: Chironomidae) from trunks in Amazon Forest (A), trunks in Cerrado (B) and leaves in Cerrado (C) producers and non-producers of xylanase (Xyl) and cellulase (CMCase).
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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.