DataType
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-16.)
DataType has 21 facts recorded in Dontopedia across 11 references, with 5 live disagreements.
21 facts·7 predicates·11 sources·5 in dispute
Mostly:rdf:type(5), has value(4), equals(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (31)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
rdf:typeRdf:type(18)
- Boolean Flag
ex:boolean-flag - Cached Data
ex:cached-data - Ex:int32
ex:ex:int32 - Float
ex:float - Float32
ex:float32 - Float32 Type
ex:float32-type - Float Array Type
ex:float-array-type - Fp16
ex:fp16 - Int Type
ex:int-type - Long
ex:long - Query Input
ex:query-input - Real World Data
ex:real-world-data - Semi Structured Data
ex:semi-structured-data - String
ex:string - Text Data
ex:text-data - Text Type
ex:text-type - Unstructured Data
ex:unstructured-data - Vector Data
ex:vector-data
assignsVariableAssigns Variable(2)
- Field Loop 0
ex:field-loop-0 - Generate
ex:generate
specifiesSpecifies(2)
- Meta Parameter
ex:meta-parameter - Np.uint8
ex:np.uint8
argumentArgument(1)
- Create Backup Call
ex:create-backup-call
checksChecks(1)
- Parse Feedback Data Function
ex:parse-feedback-data-function
criterionCriterion(1)
- Tool Selection Criteria
ex:tool-selection-criteria
hasDataTypeHas Data Type(1)
- Field
ex:field
includesIncludes(1)
- Tool Selection Factors
ex:tool-selection-factors
isExampleOfIs Example of(1)
- Sample Ranking Record
ex:sample-ranking-record
iterationVariableIteration Variable(1)
- For Loop Data Type
ex:for-loop-data-type
joinsJoins(1)
- Data Path Construction
ex:data-path-construction
typeType(1)
- Streaming Documents
ex:streaming-documents
Other facts (19)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Vector Property | [5] |
| Rdf:type | Python Class | [6] |
| Rdf:type | Concept | [7] |
| Rdf:type | Dask Data Type | [10] |
| Rdf:type | Selection Criterion | [11] |
| Has Value | str | [1] |
| Has Value | float | [1] |
| Has Value | datetime | [1] |
| Has Value | bool | [2] |
| Equals | Str Type | [3] |
| Equals | Float Type | [3] |
| Equals | Datetime Type | [3] |
| Equals | Bool Type | [3] |
| Has Variants | structured | [11] |
| Has Variants | unstructured | [11] |
| Has Variants | big-data | [11] |
| Precision | Single Precision | [4] |
| Must Be | Dictionary | [8] |
| Used by | Term Property | [9] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
—
hasValuebeam/4c7fdf93-1d3e-47fa-bd33-c0a03ee8e237
str
—
hasValuebeam/4c7fdf93-1d3e-47fa-bd33-c0a03ee8e237
float
—
hasValuebeam/4c7fdf93-1d3e-47fa-bd33-c0a03ee8e237
datetime
—
hasValuebeam/1bddda24-6839-49bd-86d8-77303c029dd6
bool
—
equalsbeam/0698efce-092d-4bc0-95dc-f5e44d2a3e37
ex:str-type
—
equalsbeam/0698efce-092d-4bc0-95dc-f5e44d2a3e37
ex:float-type
—
equalsbeam/0698efce-092d-4bc0-95dc-f5e44d2a3e37
ex:datetime-type
—
equalsbeam/0698efce-092d-4bc0-95dc-f5e44d2a3e37
ex:bool-type
—
precisionbeam/cd357396-3d15-4187-a06d-464838aefe07
ex:single-precision
—
typebeam/cdd51d1c-232b-4579-bc7b-6fee02a86cab
ex:VectorProperty
—
typebeam/58335043-7a28-4310-8bc8-6b38b5011f99
ex:PythonClass
—
labelbeam/58335043-7a28-4310-8bc8-6b38b5011f99
DataType
—
typebeam/3b85dbf9-9ffc-4bfc-ae62-d136bba6e225
ex:Concept
—
labelbeam/3b85dbf9-9ffc-4bfc-ae62-d136bba6e225
Data type for encryption
—
mustBebeam/a9ce86af-f2e4-41c0-a430-ce945f58567e
ex:dictionary
—
usedBybeam/35f6cc41-2be5-463a-be9c-95e4900404b7
ex:term-property
—
typebeam/97b0f578-1a3d-4330-a3c6-751ff8fef12c
ex:DaskDataType
—
typelme/58d34da2-c5c2-4c61-b093-2b1a9cd8298b
ex:SelectionCriterion
—
hasVariantslme/58d34da2-c5c2-4c61-b093-2b1a9cd8298b
structured
—
hasVariantslme/58d34da2-c5c2-4c61-b093-2b1a9cd8298b
unstructured
—
hasVariantslme/58d34da2-c5c2-4c61-b093-2b1a9cd8298b
big-data
References (11)
11 references
ctx:claims/beam/4c7fdf93-1d3e-47fa-bd33-c0a03ee8e237- full textbeam-chunktext/plain1 KB
doc:beam/4c7fdf93-1d3e-47fa-bd33-c0a03ee8e237Show excerpt
if 'min_value' in constraints: data_model[field] = data_model[field].apply(lambda x: max(x, constraints['min_value'])) if 'max_value' in constraints: da…
ctx:claims/beam/1bddda24-6839-49bd-86d8-77303c029dd6- full textbeam-chunktext/plain1 KB
doc:beam/1bddda24-6839-49bd-86d8-77303c029dd6Show excerpt
data_model[field] = pd.to_datetime(data_model[field], format=constraints['format']) elif data_type == 'bool': data_model[field] = data_model[field].astype(bool) …
ctx:claims/beam/0698efce-092d-4bc0-95dc-f5e44d2a3e37- full textbeam-chunktext/plain1 KB
doc:beam/0698efce-092d-4bc0-95dc-f5e44d2a3e37Show excerpt
if 'max_value' in constraints: data_model[field] = data_model[field].apply(lambda x: min(x, constraints['max_value'])) elif data_type == 'str': …
ctx:claims/beam/cd357396-3d15-4187-a06d-464838aefe07- full textbeam-chunktext/plain1 KB
doc:beam/cd357396-3d15-4187-a06d-464838aefe07Show excerpt
### Using Quantization for Efficiency Quantization can further reduce the memory footprint and speed up the search process. FAISS supports various quantization techniques, such as PQ (Product Quantization). Here's an example using PQ: ``…
ctx:claims/beam/cdd51d1c-232b-4579-bc7b-6fee02a86cabctx:claims/beam/58335043-7a28-4310-8bc8-6b38b5011f99- full textbeam-chunktext/plain1 KB
doc:beam/58335043-7a28-4310-8bc8-6b38b5011f99Show excerpt
Here's how you can set up and use Milvus to store and retrieve document embeddings: ### Step-by-Step Guide 1. **Install Milvus**: - Install Milvus using Docker or from source. - Ensure you have a running Milvus instance. 2. **Desig…
ctx:claims/beam/3b85dbf9-9ffc-4bfc-ae62-d136bba6e225- full textbeam-chunktext/plain1 KB
doc:beam/3b85dbf9-9ffc-4bfc-ae62-d136bba6e225Show excerpt
key = os.urandom(32) # 256-bit key iv = os.urandom(16) # 128-bit IV # Encrypt the data encrypted_data, key, iv = encrypt_data(data, key, iv) print(f"Encrypted data: {encrypted_data.hex()}") # Decrypt the data original_data = decrypt_dat…
ctx:claims/beam/a9ce86af-f2e4-41c0-a430-ce945f58567e- full textbeam-chunktext/plain1 KB
doc:beam/a9ce86af-f2e4-41c0-a430-ce945f58567eShow excerpt
4. **Test with Different Data Samples**: - Test the feedback loop with various data samples, including edge cases and malformed data. - Identify specific data points that consistently trigger the error. 5. **Isolate the Problematic …
ctx:claims/beam/35f6cc41-2be5-463a-be9c-95e4900404b7- full textbeam-chunktext/plain1 KB
doc:beam/35f6cc41-2be5-463a-be9c-95e4900404b7Show excerpt
First, ensure that your Elasticsearch index is correctly configured with the synonym analyzer and filter. Your current configuration looks mostly correct, but there are a few improvements and checks we can make. ### 2. Use `synonyms_path` …
ctx:claims/beam/97b0f578-1a3d-4330-a3c6-751ff8fef12c- full textbeam-chunktext/plain1 KB
doc:beam/97b0f578-1a3d-4330-a3c6-751ff8fef12cShow excerpt
Here's an example implementation using Pandas and spaCy for efficient tokenization of large datasets: ```python import spacy import pandas as pd from concurrent.futures import ProcessPoolExecutor import time # Load spaCy model nlp = spacy…
ctx:claims/lme/58d34da2-c5c2-4c61-b093-2b1a9cd8298b- full textbeam-chunktext/plain17 KB
doc:beam/58d34da2-c5c2-4c61-b093-2b1a9cd8298bShow excerpt
[Session date: 2023/05/20 (Sat) 06:16] User: I'm looking for some help with data visualization tools. I recently participated in a case competition hosted by a consulting firm, where we had to analyze a business case and present our recomme…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.