Historically, since before there was an official Chia Network-provided download, our team-member Gnomuz has provided an automatic backup of the blockchain database running around midnight CET every day, and it's then uploaded to Dropbox. This download is also still available and updated daily:
This is the first step along a much longer road that will integrate this feature into the functionality of the reference client eventually. For now, users will need to move their database files that they acquire from this path manually into place. But this should be a far more expedient and far more efficient way to get new farmers started with the blockchain.
Chia Download Database
Download File 🔥 https://tinurll.com/2yGAPK 🔥
In 2.1.0 the option to use different plotters including compressed plotter was introduced. Each plotter has slightly different hardware requirements and may need slightly different options specified.The cli reference for all plotters can be found in the Plotters CLI Page. Learn more about the alternative plotters in the Alternative Plotters page.
To create a Plot NFT, use chia plotnft create -u , entering the URL of the pool you want to use. To create a plot NFT in self-farming mode, do chia plotnft create -s local.To switch pools, you can use chia plotnft join, and to leave a pool (switch to self farming), use chia plotnft leave.The show command can be used to check your current points balance. CLI plotting with create_plots is the same as before, but the -p is replaced with -c, and the pool contract address from chia plotnft show should be used here.
First, this looks in all plot directories from your config.yaml. You can check those directories with chia plots show. This command will check whether plots are valid given the plot's associated keys and your machine's stored Chia keys, as well as test the plot with challenges to identify found plots vs. expected number of plots.
-l allows you to find duplicate plots by ID. It checks all plot directories listed in config.yaml and lists out any plot filenames with the same filename ending; *-[64 char plot ID].plot. You should use -l -n 0 if you only want to check for duplicates.
-n represents the number of challenges given. If you don't include an -n integer, the default is 30. For instance, if -n is 30, then 30 challenges will be given to each plot. The challenges count from 5 (minimum) to -n, and are not random.
The plots check challenge is a static challenge. For example if you run a plots check 20 times, with 30 tries against the same file, it will produce the same result every time. So while you may see a plot ratio
-p, --derive-from-hd-path [TEXT]: Search for items derived from a specific HD path. Indices ending in an 'n' indicate that non-observer derivation should used at that index. Example HD path: m/12381n/8444n/2/
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We present Chia, a novel, large annotated corpus of patient eligibility criteria extracted from 1,000 interventional, Phase IV clinical trials registered in ClinicalTrials.gov. This dataset includes 12,409 annotated eligibility criteria, represented by 41,487 distinctive entities of 15 entity types and 25,017 relationships of 12 relationship types. Each criterion is represented as a directed acyclic graph, which can be easily transformed into Boolean logic to form a database query. Chia can serve as a shared benchmark to develop and test future machine learning, rule-based, or hybrid methods for information extraction from free-text clinical trial eligibility criteria.
Clinical trial eligibility criteria specify rules for screening clinical trial participants and play a central role in clinical research in that they are interpreted, implemented, and adapted by multiple stakeholders at various phases in the clinical research life cycle1. After being defined by investigators, eligibility criteria are used and interpreted by clinical research coordinators for screening and recruitment. Then, they are used by query analysts and research volunteers for patient screening. Later, they are summarized in meta-analyses for developing clinical practice guidelines and, eventually, interpreted by physicians to screen patients for evidence-based care. Hence, eligibility criteria affect recruitment, results dissemination, and evidence synthesis.
A shared, sufficiently large dataset is much needed to boost machine learning natural language processing of eligibility criteria text. In this study we present Chia, a large annotated corpus of clinical research eligibility criteria from 1,000 diverse clinical studies. The annotations specify (a) the boundaries and semantic categories of named entities and (b) the Boolean operators needed to form the database query logic. As the first public large annotated corpus for clinical trial eligibility criteria, Chia can serve as a shared benchmark to develop and test future machine learning, rule-based, or hybrid methods for information extraction from free-text clinical trial eligibility criteria.
The annotation model was developed by two annotators (FK and LHF), both with medical training, and one machine learning researcher (CY), following an iterative process. The entity categories are aligned with the domain names defined by the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model (CDM), which is widely used in the medical research community for health data standardization19. Our annotation model is described in full in the Appendix. A brief description is provided below with a focus on its three main components: Entities, Relationships, and the resulting Annotation Graph.
We searched ClinicalTrials.gov on August 2, 2018 for actively recruiting, interventional (clinical trial), phase 4 studies, and obtained 2,913 trials, from which a random sample of 1,000 clinical trials was drawn. We focused on current studies as opposed to historic ones, assuming reporting quality is generally better in more current trials20, and prioritized phase 4 since they are more likely to be replicated via pure observational data analyses21. From each trial, a script downloaded and extracted every eligibility criterion (roughly defined as one line of free text), exported plain text files, and loaded them into the brat annotation tool ( ). That script is in R language and is available at -InformaticsResearch/CHIA.
The creation of Chia was performed by medical professionals (FK and LHF). Each annotator received a separate set of criteria loaded in brat22 and hand-created the entities and relationships as expressed above. In case of doubt for some concept, the annotator searched terminology at , which provides searching of concepts in the OMOP CDM. For the first 200 trials, both annotators regularly discussed adaptations to the annotation model based on their experience and re-annotated criteria as needed according to the changes being made. Once a satisfactory model was attained based on the consensus of both annotators, modifications were suspended and the task of annotation proceeded until the completion of the 1,000 trials. The final Chia dataset contains the summed and collectively revised work of the two annotators.
Minor post-production was performed to transform the ANN files produced by brat into a single long table in CSV format containing the entire dataset. That table also contains a number of variables that can be programmatically inferred from the annotations, e.g.,, which entities are roots in their annotation graphs. Additional post-processing was performed to generate two distinct datasets: one titled With Scopes and the other Without Scopes differing only in their utilization of Scope entity within the annotation model. Greater discussion of the reasoning behind the two distinct datasets is included in the Appendix, and all code used to generate these two models is available at -InformaticsResearch/CHIA.
To identify the target diseases of the 1,000 annotated trials, additional dataset enrichment was accomplished by leveraging the Aggregate Analysis of ClinicaTrials.gov (AACT) database23. This publicly available relational database contains all information about every study registered in ClinicalTrials.gov and is provided by the Clinical Trials Transformation Initiative (CTTI). The list of 1,000 unique NCT IDs included in our dataset was extracted and matched with their corresponding target conditions using the conditions table in the AACT database.
A distinguishing feature of our dataset is its capacity to support the parsing of the entities and their relationships into a Boolean expression containing the logic of the database query that replicates the eligibility criteria of each clinical trial. A sample annotation alongside its associated annotation graph and pseudo-query is provided in Fig. 1.
The free-text of selected eligibility criteria, brat configuration files, and the annotated data files are all available on figshare at There are two folders of annotation files titled With Scope and Without Scope, describing the inclusion or exclusion of Scope entities (additional information in Appendix).
Extracted free-text eligibility criteria from the 1,000 selected trials. Each text file adheres to the following naming format: [NCT Number][Inclusion/Exclusion Status].txt. Each row contains a single eligibility criterion.
The Chia dataset contains a total of 4,161 annotated Scope objects with 1,009 having an incoming subsumes or multi relationship, serving a body of original and useful knowledge for electronic phenotyping (discussed further in Use Case 2 below). A few examples of Scope objects are presented in Table 8. The average number of entities contained within these Scope objects is 3.51 with a maximum of 82. Additionally, 2,318 subsumes or multi relationships are available throughout the dataset with that number increasing to 2,521 in the dataset without Scope objects (post-Scope decomposition). 152ee80cbc
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