The NIST Extensible Resource Data Model (NERDm) is a set of schemas for encoding in JSON format metadata that describe digital resources. The variety of digital resources it can describe includes not only digital data sets and collections, but also software, digital services, web sites and portals, and digital twins. It was created to serve as the internal metadata format used by the NIST Public Data Repository and Science Portal to drive rich presentations on the web and to enable discovery; however, it was also designed to enable programmatic access to resources and their metadata by external users. Interoperability was also a key design aim: the schemas are defined using the JSON Schema standard, metadata are encoded as JSON-LD, and their semantics are tied to community ontologies, with an emphasis on DCAT and the US federal Project Open Data (POD) models. Finally, extensibility is also central to its design: the schemas are composed of a central core schema and various extension schemas. New extensions to support richer metadata concepts can be added over time without breaking existing applications. Validation is central to NERDm's extensibility model. Consuming applications should be able to choose which metadata extensions they care to support and ignore terms and extensions they don't support. Furthermore, they should not fail when a NERDm document leverages extensions they don't recognize, even when on-the-fly validation is required. To support this flexibility, the NERDm framework allows documents to declare what extensions are being used and where. We have developed an optional extension to the standard JSON Schema validation (see ejsonschema below) to support flexible validation: while a standard JSON Schema validater can validate a NERDm document against the NERDm core schema, our extension will validate a NERDm document against any recognized extensions and ignore those that are not recognized. The NERDm data model is based around the concept of resource, semantically equivalent to a schema.org Resource, and as in schema.org, there can be different types of resources, such as data sets and software. A NERDm document indicates what types the resource qualifies as via the JSON-LD "@type" property. All NERDm Resources are described by metadata terms from the core NERDm schema; however, different resource types can be described by additional metadata properties (often drawing on particular NERDm extension schemas). A Resource contains Components of various types (including DCAT-defined Distributions) that are considered part of the Resource; specifically, these can include downloadable data files, hierachical data collecitons, links to web sites (like software repositories), software tools, or other NERDm Resources. Through the NERDm extension system, domain-specific metadata can be included at either the resource or component level. The direct semantic and syntactic connections to the DCAT, POD, and schema.org schemas is intended to ensure unambiguous conversion of NERDm documents into those schemas. As of this writing, the Core NERDm schema and its framework stands at version 0.7 and is compatible with the "draft-04" version of JSON Schema. Version 1.0 is projected to be released in 2025. In that release, the NERDm schemas will be updated to the "draft2020" version of JSON Schema. Other improvements will include stronger support for RDF and the Linked Data Platform through its support of JSON-LD.

3D scans of two cacao pod models. The models were created around 1803 most likely by Franz Stoll or Johann Jaich, who were employed at the Naturalien-Cabinet, the predecessor of the Natural History Museum Vienna, as wax modellers. They are made of plaster and were coloured by hand. The models were used in the exhibit of the Naturalien-Cabinet.

See 6 further cacao pod 3D models here: https://doi.org/10.57756/jywyze.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Snapshot img

Sleeping Pods Market Size 2024-2028

The sleeping pods market size is forecast to increase by USD 45.2 million at a CAGR of 7% between 2023 and 2028.

The market is experiencing significant growth due to increasing awareness of the benefits of these pods in various sectors, particularly in workplaces. Sleeping pods offer numerous advantages, including improved productivity, reduced stress levels, and enhanced employee well-being. However, the high cost of sleeping pods remains a challenge for market expansion.
Despite this, the market is expected to continue growing, driven by technological advancements and the increasing prioritization of employee health and wellness. Companies are investing in sleeping pods as part of their employee benefits packages, recognizing the long-term benefits to their bottom line. The trend towards flexible work arrangements and the need for employees to work longer hours is also fueling demand for sleeping pods as a solution to help workers recharge during the workday.

What will be the Size of the Market During the Forecast Period?

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The market encompasses various types of compact sleeping structures, including nap pods, energy pods, nap capsules, sleeping cabins, and snooze pods. These pods cater to consumers seeking short naps during periods of travel or work. Air passengers, particularly those with long transit flights, greatly benefit from these Passenger-friendly facilities.
Moreover, the service model and business model of sleeping pods vary, with some offering pay-per-use and others requiring memberships. Non-aeronautical services, such as greeters and fax machines, may also be included in some sleeping pod businesses. The market for sleeping pods is growing as consumers increasingly prioritize comfort and efficiency in their travel experiences.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Airport
  Hospitals
  Corporate
  Others


Geography

  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  North America

    Canada
    US


  Middle East and Africa



  South America

    Brazil

By End-user Insights

The airport segment is estimated to witness significant growth during the forecast period.

Sleeping pods, also known as nap capsules or capsule hotels, have gained significant attention in various sectors, including academia and sleep science, since the early 2000s. These innovative spaces offer power naps and short-term sleep solutions for individuals in various settings. Employers have started incorporating sleeping pods in their offices to boost employee productivity and well-being. Luxury hotels and recreational facilities have also integrated these unique designs as premium services for their guests. Innovations in sleeping pods include smart controls, biometric solutions, and mobile apps that allow users to customize their experience with timers, speakers, light effects, and temperature control.

Similarly, some companies, such as Slumber Pod and Homebase, offer discounts, special offers, and free trials to attract customers. These sleeping pods provide a sound-proofed and temperature-controlled environment for individuals to recharge, making them an attractive alternative to traditional sleeping arrangements. Airports have embraced sleeping pods as part of their non-aeronautical services, catering to the needs of air passengers and transit flyers. The service model and business model of airport sleeping pods include concierge services, restaurants, and additional services to enhance the customer experience. With the rise in the number of air passengers and transit flights, the demand for airport sleeping pods is projected to increase, making them an essential component of smart airports.

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The airport segment was valued at USD 51.00 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 50% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

Europe is a significant market for nap pods, energy pods, and sleeping cabins, accounting for a substantial share due to the increasing demand from various industries. The region's market growth is fueled by high consumer disposable income, advanced technology access, and heightened awareness of the benefits of these pods. In Europe, the use of s

Chemical-induced alteration of maternal thyroid hormone levels may increase the risk of adverse neurodevelopmental outcomes in offspring. US federal risk assessments rely almost exclusively on apical endpoints in animal models for deriving points of departure (PODs). New approach methodologies (NAMs) such as high-throughput screening (HTS) and mechanistically informative in vitro human cell-based systems, combined with in vitro to in vivo extrapolation (IVIVE), supplement in vivo studies and provide an alternative approach to calculate/determine PODs. We examine how parameterization of IVIVE models impacts the comparison between IVIVE-derived equivalent administered doses (EADs) from thyroid-relevant in vitro assays and the POD values that serve as the basis for risk assessments. Pesticide chemicals with thyroid-based in vitro bioactivity data from the US Tox21 HTS program were included (n = 45). Depending on the model structure used for IVIVE analysis, up to 35 chemicals produced EAD values lower than the POD. A total of 10 chemicals produced EAD values higher than the POD regardless of the model structure. The relationship between IVIVE-derived EAD values and the in vivo-derived POD values is highly dependent on model parameterization. Here, we derive a range of potentially thyroid-relevant doses that incorporate uncertainty in modeling choices and in vitro assay data.

Additional tables comprising data for feature encodings, model parameters, and selected feature sets.

The attached file has all the data used in the change-point and MAR manuscripts.

The first sheet "variables" has the estimated values for each variable, the second sheet "SEs" the standard errors. The 3rd sheet has definitions and some explanations - tables in the MAR manuscript probably supersede these.

The final sheet "Changepoint covariate matrix" is a matrix indicating which variables were candidates in the variable selection models in change-point (this info and related MAR info is in the manuscripts).

The GRP Bathroom Pods market is poised for significant expansion, projected to reach an estimated market size of $1.5 billion by 2025, with a robust Compound Annual Growth Rate (CAGR) of 7.2% during the forecast period of 2025-2033. This growth is primarily propelled by an increasing demand for efficient and sustainable construction solutions across various sectors. The residential sector is emerging as a dominant application, driven by the need for faster project completion times, consistent quality, and reduced on-site labor, especially in urbanized areas facing housing shortages. Furthermore, the hospitality industry's continuous development and renovation projects, coupled with the growing adoption of prefabricated elements in institutional settings like schools and healthcare facilities, are also acting as substantial growth catalysts. The inherent benefits of GRP pods, including their durability, water resistance, lightweight nature, and ease of installation, align perfectly with modern construction paradigms emphasizing speed, cost-effectiveness, and environmental responsibility. Several key trends are shaping the GRP Bathroom Pods market landscape. The ongoing evolution towards off-site construction and modular building techniques is a primary driver, offering significant advantages over traditional methods. Advancements in manufacturing technologies are leading to more sophisticated designs, improved aesthetics, and enhanced functionality of GRP pods, making them increasingly attractive to architects and developers. The emphasis on sustainability and eco-friendly building practices further bolsters the market, as GRP pods can contribute to reduced waste and lower embodied carbon. However, the market also faces certain restraints, including initial investment costs for manufacturing facilities and the need for greater standardization and regulatory acceptance in some regions. Despite these challenges, the inherent advantages and growing acceptance of GRP bathroom pods position the market for sustained and dynamic growth in the coming years, with North America and Europe currently leading in adoption, while the Asia Pacific region is expected to witness the fastest growth due to rapid urbanization and infrastructure development. Here's a report description on GRP Bathroom Pods, incorporating your specified details and structure:

This comprehensive report offers an in-depth analysis of the global GRP (Glass Reinforced Plastic) Bathroom Pods market, providing critical insights into its evolution, key drivers, challenges, and future trajectory. Leveraging extensive historical data from 2019-2024 and a robust projection model, the report anchors its analysis in the base year of 2025 and extends its forecast through 2033. With an anticipated market size reaching into the millions of units, this study is designed for stakeholders seeking to understand and capitalize on the burgeoning opportunities within this specialized construction sector.

Simulation data generated with the Dune model dune-ax1. Currently, there is data on which my 2013 Biophysical Journal paper is based on.

Structure:

The simulation data contains a main folder with mostly gnuplot data files and some debug/diagnostics information. The subfolder 'hdf5' contains values of alle the unknowns as well as domain information for each time step.

Contact:

If you have questions or if you are interested in data from different simulation setups, please contact me on Github: https://github.com/pederpansen/dune-ax1.

The file called Splus code for generating MAR covariates.txt contains the code used to extract the data, and some functions that are used in the code. Data sets are either provided in this folder as well (mostly in a single spreadsheet "MAR raw and ancillary data") or there is a description of how to get them (from Web sites and other sources). Contact Wim Kimmerer kimmerer@sfsu.edu if there are problems with the code or undefined functions.

File List code_and_data.rar (md5: 73050eb6839d36696bd85ae3fabd6c8c) data.plot.txt (md5: 0fc94d989c075f014fa0a88dcfe336c8) data.subplot.txt (md5: 628b79084eb68ef06a577673fa809b9f) data.tree.txt (md5: 9de3ce8e00f72728a6638e9a51546602) BUGS.mA.annotated.txt (md5: 396e6ff35a8f02db566be9b304dc6c3c) BUGS.mB.annotated.txt (md5: c2238f71556018781c2f0b1376a0626c) Rcode.modelA.annotated.R (md5: 4662b653ba1fbc26a4de371d5ec6efb4) Rcode.modelB.annotated.R (md5: a4a5ab4dfe94beb29f862d4a53ae3092) effects.modelB.annotated.R (md5: 3f87f050daafb00fa2b5c1cfeed6f7ce) Rcode.dsep.annotated.R (md5: 17bb4eabeee5c84a14693a766faf8af9) claim1_bugs.txt (md5: 86a38928ffe6ffc4ed942d77f560ae70) claim2_bugs.txt (md5: 44654a248baee405daa95bf47bf1f323) claim3A_bugs.txt (md5: c0c6eab9e48e1c2928fe46029cc1e167) claim3B_bugs.txt (md5: a385df60ef6da65b6572cc310fc754e8) claim4_bugs.txt (md5: fea7e182b32ea77eb71d7295645bfca8) claim5_bugs.txt (md5: 486ea08f4ed704ccfae27e62039458b0) claim6_bugs.txt (md5: 46efa7b9c905c54a9d1cd77c81f09688) claim7A_bugs.txt (md5: 8e36c5443e9a63fbf7fe52912f18dcba) claim7B_bugs.txt (md5: c4e370e2574700c2b2b20db3e985a847) claim8A_bugs.txt (md5: 25cbcbae615680c5731fa6a3ba294905) Description code_and_data.zip is an archive containing all data and code described below (19 files). data.plot.txt is a text file containing data at plot scale. Column definitions are:

    plot: plot number, 1–43
    temp: mean daily temperature in °C
    age: tree age in years

  data.subplot.txt is a text file containing data at plot scale. Column definitions are:

    subplot: subplot number, 1–86
    plot: plot number, 1–43
    Nfert: nitrogen fertilization, dummy variable (1 = fertilized, 0 = control)

  data.tree.txt is a text file containing data at plot scale. Column definitions are:

    tree: tree number, 1–430
    subplot: subplot number, 1–86
    plot: plot number, 1–43
    Pc: presence of Philidris cf. cordata, dummy variable (1 = present, 0 = absent)
    Hs: number of pods with Helopeltis sulawesi damage
    Cc: number of pods with Conopomorpha cramerella damage
    Npodh: number of harvested cacao pods

  The data files BUGS.mA.annotated.txt and BUGS.mB.annotated.txt contain the BUGS code for models A and B respectively. These BUGS files, as well as the data files (files 1 to 3) have to be in the working directory of R when running the R code contained in the files Rcode.modelA.annotated.R and Rcode.modelB.annotated.R. The R package R2WinBUGS is needed as its function bugs() is called upon by the R code to run the models. The R code to compute the direct and indirect effects in model B is contained in effects.modelB.annotated.R. This code will only run if model B was run successfully, which causes the samples of the posterior distribution to be available in the R workspace. Rcode.dsep.annotated.R contains the R code to compute d-sep test for models A and B. It runs the BUGS code associated with the independence claims implied by model A and B, which is contained in the files claim1_bugs.txt, claim2_bugs.txt, claim3A_bugs.txt, claim3B_bugs.txt, claim4_bugs.txt, claim5_bugs.txt, claim6_bugs.txt, claim7A_bugs.txt, claim7B_bugs.txt, and claim8A_bugs.txt.

This project integrates analysis of traditional fire management methods with powerful modeling platforms to create a framework for working collaboratively to prioritize fuel treatments, manage wildfires, identify and restore cultural resources and fire use, and create fire resilient forests and communities. Three key datasets were created: A network of potential control locations that form fire management containers called Potential Operational Delineations, or PODs, were developed across the NCRP region. PODs can be used as a risk management and planning tool for wildfire suppression, prescribed and cultural fire, and hazardous fuel mitigation. A cultural ignition dataset for the landscape surrounding Somes Bar and Orleans in California was created by simulating the location, timing, and frequency of Indigenous burns. This dataset can be integrated into landscape fire models to make them more accurate and answer deeper questions about the effect of cultural fire across the landscape.A draft of an ecologically and culturally nuanced State and Transition Model (STM), which shows how vegetation and fuels change over time under different fire management scenarios, was drafted for a 600k+ acre landscape surrounding Somes Bar and Orleans in California.

Proof Of Delivery

## Overview

Proof Of Delivery is a dataset for object detection tasks - it contains Doors annotations for 869 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

The first set of WinBugs scripts, R scripts, and input data created by POD project analyst Jim Thompson to generate the first Change Point Analysis study in July,2008.

Following is the text of the electronic mail mesasge sent by Dr. Thompson with the scripts and data files:

as promised, heres the first lot of changepoint files for archiving:

"poddata.txt" is (funnily enough) the data, in tab delim format for reading into R- its basically a subset of the "nceas_data_one.xls" file discussed previously.

The remaining .txt files are WinBUGS model codes - 1 for each of 4 alternative prior specifications....following Kens suggestion I've been doing some sensitivity analyses - comparing different priors for changepoint magnitudes. These are the models used for that - and the "gamma" one is the one used for the initial anaylses.

The .R file has the R script that automates fitting all these models for all the response variables and collating (and plotting) the results. Its not very well "commented" yet, but most of it would be fairly self-explanatory to an experience R and WinBUGS user (I can't imagine anyone else wanting to use it).

A corresponding set of files for models with covariates will follow......

cheers Jim

PS -*WARNING * just in case any of you are mad enough to actually run this script - the R script is set to run through 12 prior specifications (4 types, 3 scaling parameters for each) for many species - so it would run for a very long time - remove some of the loops before running if you want to try it (I've been doing it for a couple of species only).

PPS Ken, I will send you some results comparing priors later next week.

ramet-based demographic and trait dataFile contains data for 2014 and 2015 that were used to parameterize the vital rate functions. Contains both demographic and trait data.fulldata.csvspatial location data for ramets of A. syriacaFile contains spatial location data along 1 m wide transects for 2014 and 2015 for Asclepias syriaca ramets.mapdata.csvnumber of seeds per pod for A. syriacaFile contains seed count data for individual pods on Asclepias syriaca ramets.seeddata.csvdemographic data for ramets of A. syriacaFile contains demographic data collected between 2013-2017 at 7 sites for A. syriaca rametsstemdata.csvhttps://doi.org/10.5281/zenodo.2803738Code for "The demographic effects of functional traits: An integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs"

The start (from) and end (to) of each combination is introduced as well. TI data is included for all models.

The effect size is defined as |log(OR)| and calculated on the training set using univariate linear logistic regression. The effect direction is indicated by (+)/ (–). Time invariant (TI), preoperative (T1), intraoperative (T2) and postoperative (T3) variables are included. The OR 95% confidence interval (CI) serves as an uncertainty estimate.

Number of extracted variables per time phase (T1-T3, TI), and per clinical domain.

Legume pods serve important functions during seed development and are themselves sources of food and feed. Compared to seeds, the metabolism and development of pods are not well-defined. The present characterization of pods from the model legume Lotus japonicus, together with the detailed analyses of the pod and seed proteomes in five developmental stages, paves the way for comparative pathway analysis and provides new metabolic information. Proteins were analyzed by two-dimensional gel electrophoresis and tandem-mass spectrometry. These analyses lead to the identification of 604 pod proteins and 965 seed proteins, including 263 proteins distinguishing the pod. The complete data set is publicly available at http://www.cbs.dtu.dk/cgi-bin/lotus/db.cgi, where spots in a reference map are linked to experimental data, such as matched peptides, quantification values, and gene accessions. Identified pod proteins represented enzymes from 85 different metabolic pathways, including storage globulins and a late embryogenesis abundant protein. In contrast to seed maturation, pod maturation was associated with decreasing total protein content, especially proteins involved in protein biosynthesis and photosynthesis. Proteins detected only in pods included three enzymes participating in the urea cycle and four in nitrogen and amino group metabolism, highlighting the importance of nitrogen metabolism during pod development. Additionally, five legume seed proteins previously unassigned in the glutamate metabolism pathway were identified.

A crop simulation model is adopted to calculate the potential yield in a certain location. The data sets generated in each scenario (2021–2022) were used to evaluate the InfoCrop model. A field experiment using a randomized complete block design was conducted at the Agronomy Department’s research field, Hajee Mohammad Danesh Science and Technology University. The following two factors: 1) factor A: sowing dates (Planting date 1: PD1 = 5th November and Planting date 2: PD2 = 15th November 2021) and 2) factor B: Trichoderma biofertilizers (T1 = control, T2 = 50% chemical fertilizer + 2,000 kg ha-1 Trichoderma biofertlizer, T3 = fully chemical fertilizer; and T4 = fully 3,000 kg ha-1 Trichoderma biofertilizer). Three BARI (Bangladesh Agricultural Research Institute) released varieties (V1 = BARI Sarisa-14, V2 = BARI Sarisa-16, and V3 = BARI Sarisa-17) used for the completion of the experiment. The Trichoderma biofertilizer and planting dates had a significant influence on yield and yield attributes of mustard. Results showed that plant height, leaf width, leaves per plant, pods per plant, harvest index, maturity date, and yield were significantly affected by Trichoderma biofertilizer treatments, two different conditions, and varieties. The regression analysis indicated a significant linear relationship between two different growing conditions especially for harvest index PD2>PD1 (0.88>0.83), grain yield (0.94>0.90), flowering date (0.95>0.91) and maturity date (0.95>0.90). It was found that the model significantly overestimated all the parameters with an acceptable error range (