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#inventory

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PartsBox

New in PartsBox: Enhanced BOM management features!

Import BOMs from CSV into new or existing projects with improved column mapping. Match BOM entries using ID Anything™ codes or CAD keys for accuracy. Merge multiple entries of the same part or expand entries with multiple designators into individual lines.

Available in all plans including Hobbyist/Maker. #Electronics #Inventory #BOM

See the blog post for more details: partsbox.com/blog/improved-bom

partsbox.comBOM Management: Import, Match and Organize Parts ListsLearn how to import BOMs, match entries to parts using ID Anything™ codes and CAD keys, and organize entries through merging and expansion.
Public
Fixstern

Finally 1.0 happened. My inventory management now has all the features planned for 1.0 five years ago...

If you're interested in a visual inventory management system for small parts try it if you want: github.com/dunkelstern/invento

There are a lot of screenshots linked in the Readme so you can make some sense of it.

If you don't want to use github, use my forgejo instance: git.dunkelstern.de/dunkelstern

GitHubGitHub - dunkelstern/inventory: Visual inventory management system for small partsVisual inventory management system for small parts - dunkelstern/inventory
#django#python#webdev
Public
💧🌏 Greg Cocks

Mapping Historical Forest Biomass For Stock-Change Assessments At Parcel To Landscape Scales
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doi.org/10.1016/j.foreco.2023. <-- shared paper
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phys.org/news/2022-06-historic <-- shared technical article
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dx.doi.org/10.1126/science.abk <-- shared paper
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#GIS #spatial #mapping #Landsat #LiDAR #AbovegroundBiomass #MachineLearning #LandCover #biomass #spatialanalysis #spatiotemporal #historical #parcel #landscape #Landtrendr #landsat #timeseries #machinelearning #algorithm #model #modeling #field #fieldwork #inventory #LiDAR #NewYork #forest #vegetation #carbonstocks #carbonaccounting #temporal #opendata #opensource #climatechange #planning #management

photo - base of a moss-covered tree in a forest
schematic - A flowchart diagram showing the key elements of the modeling and mapping methodology. Cylinders represent data repositories, parallelograms represent data products and results, rectangles represent processing steps, and ovals represent models.
maps - New York State (USA) AGB difference map (2019 AGB - 1990 AGB) with predictions from the ensemble model. a) Statewide scale. b) A mosaic of working forests and Adirondack Forest Preserve land south of Stillwater Reservoir, NYS. c) Allegany River area with a portion of Allegany State Park to the east of the river. d) Forest growth along the border of the Catskill Forest Preserve. Values are capped at ± 75 Mg ha−1 for display.
maps - A qualitative comparison of maps from each modeling approach within Huntington Wildlife Forest (boundary mapped with black box in NAIP panel) and the surrounding area in Newcomb, New York (full area extent mapped with black box in New York State panel). Pair-wise raster subtractions (values capped at ± 75 Mg ha−1 for display) highlight spatial patterns and magnitudes of differences between model predictions. Ensemble - Direct not shown because it duplicates Indirect - Ensemble. A 1 m LiDAR-derived canopy height model and 0.5 m natural color National Aerial Imagery Program (NAIP) orthophotography included for additional reference information. All surfaces represent conditions in 2015.
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Podcast Projets libres !

In July 2023, I recorded a two-episode series about the history of the #GLPI project.

An English translation of the transcript is now available here: projets-libres.org/en/glpi-tur

In this first episode, with its former and current maintainers, we talk about the community years, from its inception to the governance change.

#podcast#translation#opensource
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💧🌏 Greg Cocks

USGS Releases ‘The 3D National Topography Model [3DTNM] Call For Action - Part 1: The 3D Hydrography Program [3DHP]’
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usgs.gov/news/technical-announ <-- technical announcement
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pubs.usgs.gov/publication/cir1 <-- USGS Circular 1519 publication link
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usgs.gov/3DHP <-- shared USGS 3DHP home page
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internetofwater.org/ <-- shared Internet of Water home page
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[ nice to see this, although I am maybe biased ~wink~ ]
#GIS #spatial #mapping #water #hydrology #surfacewater #3D #opendata #3DHP #3DEP #EDH #3DTNM #elevationderivedhydrology #topography #hydrography #CallForAction #publicdata #HYFeatures #opensource #IOW #InternetOfWater #DEMs #digitalinfrastructure #waterresources #resourcemanagement #inventory #planning
@USGS

Maps showing the improved alignment possible in the 3D Hydrography Program by using hydrography data derived from high-quality elevation data from the 3D Elevation Program (3DEP). The example maps show topography and streams near the border between Skagit and Snohomish Counties in Washington.
map - Example of hydrography derived from 3DEP lidar data for a section of the Marshyhope Creek near Federalsburg, Maryland.
Block diagram showing elevation data being collected over natural and human-made environments. Built using high- resolution 3D Elevation Program and 3D Hydrography Program data, the 3D National Topography Model (3DNTM) will incorporate other critical landscape information such as landcover, structures, transportation, and geology to inform a comprehensive understanding of the landscape to enable data-driven decisions and science. The 3DNTM aspires to augment today’s airborne-acquired elevation data with data acquired from a variety of platforms including satellite, drone, and potentially others. Hydrography data for lakes, rivers, and reservoirs will be derived from that elevation data to form an integrated representation of the landscape that can be connected with other water features such as wetlands and groundwater.
Digial elevation model of USGS site 365149109492300 Laguna Creek below Dennehotso near Kayenta, AZ.
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💧🌏 Greg Cocks

U.S. Landslide Inventory and Susceptibility Map [updated, USGS]
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usgs.gov/tools/us-landslide-in <-- shared technical resource, with map and county-level metrics are available as structured data
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alturl.com/ztxbs <-- shared link to USGS Landslide Susceptibility WebMap
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usgs.gov/media/videos/parsimon <-- shared video overview
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doi.org/10.1029/2024AV001214 <-- shared paper
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doi.org/10.5066/P13KAGU3 <-- shared 2024 USGS technical publication
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#GIS #spatial #mapping #model #modeling #spatialanalysis #geology #engineeringgeology #massmovement #landslide #spatialanalysis #historicdata #webmap #USA #3dep #elevation #topography #geomorphometry #geomorphology #risk #hazard #riskreduction #suspectibility #opendata #remotesensing #database #compilation #inventory #terrain #landuse #planning
@USGS

screenshot - USGS landslide susceptibility webmap
Map of the United States and Puerto Rico (with state abbreviations shown) comparing percentage of landslide susceptible terrain from the N10 model to density of mapped landslides from the national inventory compilation (Belair et al., 2022) for each U.S. county.
Map of landslide susceptibility (∼90-m) for (a) conterminous United States (CONUS), (b) Alaska, (c) Puerto Rico, and (d) Hawaii based on our nonlinear threshold N10 model with a 10th percentile cutoff. Cell colors note the number of 10-m cells within a 90-m grid cell where the slope-relief threshold is exceeded. Cell counts are classified using equal interval bins. Susceptibility maps do not account for the likely frequency or magnitude of landslides; this map reflects the degree to which local topographic conditions are consistent with our previously observed landslides across the country.
Oblique aerial photograph of the 2014 landslide in northwest Washington. This image shows the entire extent of the landslide source area and path. This event is commonly named the “Oso Landslide” in many official reports.
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