The lidar survey was conducted by vendor Parametrix, a Lidar company. Lidar instrument Optech ALTM-2033 was flown with Cessna 337 over the period of 13 and 24 Oct 2007. The data were delivered in ASCII format with information on Ground Last Return, Extracted Features 1st Return, Extracted Features Last Return, All Shots 1st Return, All Shots Last Return, Model Keypoints. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 5741 hectares in Jack Waite Mine, Murray,Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components.

The lidar survey was conducted by vendor Spectrum Mapping LCC, a Lidar company. Lidar instrument Datis II sensor was flown over the period of 10 and 12 July 2004. The data were delivered in ASCII format (files separated by vegetation points and ground points) with information on X, Y, elevation, Return number, RGB and scan angle. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 13234 hectares in Emerald Creek, St. Joe National Forest, Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components.

The lidar survey was conducted by vendor Earth Eye LLC, 3680 Avalon Park Blvd. The data were delivered in LAS 1.1 format with information on return number, easting, northing, elevation, scan angle, and intensity for each return. This project is the data acquisition phase of a administrative study being done in collaboration with the Nez Perce National Forest, Grangeville, ID; Forest Service Region 1 Regional Office, Missoula, MT (Forest Inventory and Analysis and Remote Sensing/ Geospatial Team); and Rocky Mountain Research Station - Forest Sciences Lab, Moscow, ID. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 17, 325 hectares in north central Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components. Clear Creek is a tributary of the Middle Fork Clearwater River located east of Kooskia, Idaho. Vegetation is variable, transitioning from low elevation shrubland and mixed conifers to upper elevation spruce-fir. Ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) are the predominant species at lower to mid elevations occupying a fairly xeric setting transitioning to grand fir (Abies grandis) and western red cedar (Thuja plicata) at mid elevations and subalpine fir (Abies lasiocarpa) at the higher elevations.

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The lidar survey was conducted by vendor Spencer B. Gross, 13545 NW Science Park dr.,97229, Portland, OR. Lidar instrument Leica ALS40 was flown over the period of 13 and 26 July 2002. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of four blocks totaling 24, 729 hectares in Nez Perce Reservation, which is located in central Idaho and is a land of beautiful rivers and steep mountains.
The data were delivered in ASCII format with information on easting, northing and elevation for each Lidar pulse. The ascii files were converted to las format and classified using the Multiscale Curvature Classification (MCC) method of Evans and Hudak (2007). Evans, J.S., and A.T. Hudak. (2007) A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4): 1029-1038.

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Lidar instrument was flown with a Leica ALS50 Lidar system over the period of September 29 - October 3, 2006. This project is the data acquisition phase of a administrative study being done in collaboration with the Nez Perce National Forest, Grangeville, ID; Forest Service Region 1 Regional Office, Missoula, MT (Forest Inventory and Analysis and Remote Sensing/ Geospatial Team); and Rocky Mountain Research Station - Forest Sciences Lab, Moscow, ID. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of two contiguous blocks totaling 42,889 hectares in north central Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components. Vegetation is variable, transitioning from low elevation shrubland and mixed conifers to upper elevation spruce-fir. Ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) are the predominant species at lower to mid elevations occupying a fairly xeric setting transitioning to grand fir (Abies grandis) and western red cedar (Thuja plicata) at mid elevations and subalpine fir (Abies lasiocarpa) at the higher elevations.

The lidar survey was conducted by vendor Horizon's, 3600 Jet Drive, Rapid City, South Dakota. Lidar instrument was flown in a Leica ALS40 on July 23, August 11 or September 22, 2003. The data were delivered in ascii format with information on return number, easting, northing, elevation and intensity for each return. The ascii files were converted to las format and classified using the Multiscale Curvature Classification (MCC) method of Evans and Hudak (2007). This project is the data acquisition phase of an administrative study being done by Rocky Mountain Research Station - Forest Sciences Lab, Moscow, ID. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The St. Joe National Forest area consists of one contiguous block totaling ~ 55684 hectares in north central Idaho, between Deary and Clarkia. The project area consists of moderately variable topographic configurations with diverse vegetation components.

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These LAS and associated files cover areas of the Payette and Boise National Forests, Idaho, and were collected for and processed by the USDA Forest Service, Moscow, ID in cooperation with Watershed Sciences Inc. of Corvallis Oregon, as a part of a project to use Lidar to study wildfire effects on forest canopy structure and hillslope erosion patterns. Data included with this set include the original .las data tiles, a shapefile delimiting the extent and placement of all of these tiles, another shapefile delimiting the total extent (boundary) of the data , and a vendor's report in PDF format detailing the data collection, processing, and error-checking steps. The data are in LAS 1.0 format with information on return number, easting, northing, elevation, scan angle, number of returns of given pulse, intensity, user data, point source ID, and GPS time.

This dataset does not contain any resources hosted on data.gov.au. It provides a link to the location of the Indigenous Land Corporation Freedom of Information (FOI) disclosure log to aide in information and data discovery. You can find the FOI Disclosure log here and the Agency's Information Publication Scheme here.

The data.gov.au team is not responsible for the contents of the above linked pages.

Files pertaining to data analyses performed and presented in the preprint, 'Porcine intestinal innate lymphoid cells and lymphocyte spatial context revealed through single-cell RNA sequencing' by Wiarda et al. 2022 are provided in this dataset. Single cell suspensions enriched for lymphocytes were obtained from ileum of two seven-week-old pigs and subjected to single-cell RNA sequencing (scRNA-seq). Peripheral blood mononuclear cells (PBMCs) were collected and processed for scRNA-seq in parallel. scRNA-seq was performed to provide transcriptomic profiles of lymphocytes in porcine ileum, with 31,983 cells annotated into 26 cell types. Deeper interrogation of data revealed previously undescribed cells in porcine intestine, including SELLhi γδ T cells, group 1 and group 3 innate lymphoid cells (ILCs), and four subsets of B cells. Single-cell transcriptomes in ileum were compared to those in porcine blood, and subsets of activated lymphocytes were detected in ileum but not periphery. Comparison to scRNA-seq human and murine ileum data revealed a general consensus of ileal lymphocytes across species. Lymphocyte spatial context in porcine ileum was conferred through differential tissue dissection prior to scRNA-seq. Antibody-secreting cells, B cells, follicular CD4 αβ T cells, and cycling T/ILCs were enriched in ileum with Peyer’s patches, while non-cycling γδ T, CD8 αβ T, and group 1 ILCs were enriched in ileum without Peyer’s patches. Data files included herein are .h5seurat files of the various cell subsets included in analyses of the manuscript. Files may be used to reconstruct different analyses and perform further data query. Scripts for original data analyses are found at https://github.com/USDA-FSEPRU/scRNAseq_Porcine_Ileum_PBMC. Raw data are available at GEO accession GSE196388. Data are available for online query at https://singlecell.broadinstitute.org/single_cell/study/SCP1921/intestinal-single-cell-atlas-reveals-novel-lymphocytes-in-pigs-with-similarities-to-human-cells. Resources in this dataset:Resource Title: Ileum_AllCells. File Name: Ileum_AllCells.tarResource Description: .h5seurat object of all the cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: GutBlood_IntegratedILCs. File Name: GutBlood_IntegratedILCs.tarResource Description: .h5seurat object of ILCs derived from both ileum and PBMC samples. Untar into .h5seurat file before use.Resource Title: Ileum_Bonly. File Name: Ileum_Bonly.tarResource Description: .h5seurat object of B cells and antibody-secreting cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_CD4Tonly. File Name: Ileum_CD4Tonly.tarResource Description: .h5seurat object of non-naive CD4 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_gdCD8Tonly. File Name: Ileum_gdCD8Tonly.tarResource Description: .h5seurat object of gd and CD8 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_ILConly. File Name: Ileum_ILConly.tarResource Description: .h5seurat object of innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_MyeloidOnly. File Name: Ileum_MyeloidOnly.tarResource Description: .h5seurat object of myeloid lineage leukocytes derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_NonImmuneOnly. File Name: Ileum_NonImmuneOnly.tarResource Description: .h5seurat object of non-immune cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_TILConly. File Name: Ileum_TILConly.tarResource Description: .h5seurat object of all T cells and innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: PBMC_AllCells. File Name: PBMC_AllCells.tarResource Description: .h5seurat object of all cells derived from PBMC samples. Untar into .h5seurat file before use.

Natural killer (NK) cells are innate immune effectors with considerable heterogeneity and potent antitumor capabilities. Intraepithelial ILC1 (ieILC1)-like NK cells, a population of cytotoxic tissue-resident innate lymphoid cells, have recently been documented in the microenvironment of head and neck squamous cell carcinomas (HNSCC) and other solid tumors. These cells have antitumor cytolytic potential and are potent producers of type 1 cytokines, including IFNγ. Here, we identify a subpopulation of ex vivo differentiated ieILC1-like NK cells that produce IL-13 upon stimulation. Functional characterization revealed that these cells co-expressed IFNγ and IL-13 while maintaining an ILC1 transcriptional signature. IL-13 was induced either upon co-culture with tumor cell lines, or in response to TGF-β and IL-15. IL-13-expressing ieILC1-like NK cells were identified among tumor infiltrating lymphocytes expanded from patient HNSCC tumors, in support of their in vivoexistence in primary tumors. These data demonstrate additional heterogeneity within the ieILC1-like NK cell population than previously appreciated and highlight a unique form of ILC plasticity in which cells with clear ILC1 transcriptional profiles express a type 2 cytokine. With the known roles of IL-13 in cancer cell growth dynamics and immunoregulation, the identification of this subset within tumor microenvironments presents a potential target for therapeutic manipulation.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The dataset contains data from an interlaboratory comparison conducted in EURAMET in 2019-2021, with the final report published in Oct 2023. The report and the ILC protocol are available in a different upload.

This dataset consists of 3 files:

participant information.xlsx: information on the setups used by each participant, their role in the ILC, temperatures measured at, the probes they measured on, the volume of chambers and the nature of the thermostat (bath, climate chamber or a subchamber).

preprocessed data and drift.xlsx: preprocessed probe resistance, reference temperature, link corrected resistance, unilateral degree of equivalence, normalised error, pressure (if available), humidity (if available), pilot measurements in liquid and air for each probe, the list of points excluded from the consensus computations.

selfheating.xlsx: probe-by-probe selfheating data from a number of participants (but not all).

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.

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This is collection level metadata for LAS files collected by USDA - Forest Service as a part of a larger project to use Lidar for forestry application. The lidar survey was conducted by vendor Horizon's, 3600 Jet Drive, South Dakota. Lidar instrument was flown with Leica ALS40 over the period of Aug, 13-14, 2003. The data were delivered in ASCII format with information on return number, easting, northing, elevation and intensity for each return. The ascii files were converted to las format and classified using the Multiscale Curvature Classification (MCC) method of Evans and Hudak (2007). The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 32708 hectares in Moscow Mountain, Moscow, north central Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components.