Scientifiques et spécialistes de l'environnement, y compris santé

Sciences de la vie, physiques et sociales · Compétences professionnelles · SOC 19-2041 · 844 skills

Contexte du métier

Ces tâches O*NET décrivent le métier et donnent le contexte de la correspondance SOC ; elles ne signifient pas que chaque Skill de la liste accomplit toutes les tâches.

Communicate scientific or technical information to the public, organizations, or internal audiences through oral briefings, written documents, workshops, conferences, training sessions, or public hearings.
Monitor effects of pollution or land degradation and recommend means of prevention or control.
Collect, synthesize, analyze, manage, and report environmental data, such as pollution emission measurements, atmospheric monitoring measurements, meteorological or mineralogical information, or soil or water samples.

Les tâches O*NET sont affichées dans leur langue source (anglais). Les données sur les tâches sont extraites de la O*NET® 29.1 Database de l’USDOL/ETA et utilisées sous CC BY 4.0.

Sources représentatives

Un échantillon limité de Skills populaires associés à ce métier, avec au plus un Skill par dépôt GitHub. La liste complète suit ci-dessous.

brycewang-stanford/Auto-Empirical-Research-Skills

5 ecology & environmental science skills. Trigger: biodiversity surveys, species data, environmental monitoring. Design: field data collection, spatial analysis, and conservation biology workflows.

mvanhorn/printing-press-library

Use OpenAQ and AirNow source-backed recipes for air-quality snapshots, nearby monitors, sensor history, point comparison, and AirNow AQI setup/current observations. Trigger phrases: air quality, OpenAQ, AirNow, AQI, PM2.5, nearby monitor, air-quality-pp-cli.

gamma-phase-associator

benchflow-ai/skillsbench

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

Tous les Skills associés

Parcourez l’association complète par popularité du dépôt ou activité récente de la source.

Les noms et descriptions des Skills restent dans la langue fournie par leur dépôt source.

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brycewang-stanford/Auto-Empirical-Research-Skills

5 ecology & environmental science skills. Trigger: biodiversity surveys, species data, environmental monitoring. Design: field data collection, spatial analysis, and conservation biology workflows.

climate-modeling-guide

brycewang-stanford/Auto-Empirical-Research-Skills

Climate simulation, modeling tools, and climate data analysis methods

mvanhorn/printing-press-library

Use OpenAQ and AirNow source-backed recipes for air-quality snapshots, nearby monitors, sensor history, point comparison, and AirNow AQI setup/current observations. Trigger phrases: air quality, OpenAQ, AirNow, AQI, PM2.5, nearby monitor, air-quality-pp-cli.

gamma-phase-associator

benchflow-ai/skillsbench

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

benchflow-ai/skillsbench

An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

seisbench-model-api

benchflow-ai/skillsbench

An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.

seismic-picker-selection

benchflow-ai/skillsbench

This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.

flood-detection

benchflow-ai/skillsbench

Detect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.

nws-flood-thresholds

benchflow-ai/skillsbench

Download flood stage thresholds from NWS (National Weather Service). Use when determining flood levels for USGS stations, accessing action/minor/moderate/major flood stages, or matching stations to their flood thresholds.

usgs-data-download

benchflow-ai/skillsbench

Download water level data from USGS using the dataretrieval package. Use when accessing real-time or historical streamflow data, downloading gage height or discharge measurements, or working with USGS station IDs.

benchflow-ai/skillsbench

Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters.

glm-calibration

benchflow-ai/skillsbench

Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE between simulated and observed temperatures.