Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type list<item: string> to null
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type list<item: string> to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
accepted_answer dict | answer_scores list | answers list | attempted_method_if_explicit string | author_attribution dict | code_blocks list | content_license string | domain string | error_text_if_explicit null | group_id string | id string | language list | library_or_tool list | problem_text string | provenance dict | question_html string | question_title string | selection_rule string | source_url string | split string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | [
{
"answer_id": 115428,
"score": 2
},
{
"answer_id": 115542,
"score": 2
}
] | [
{
"answer_html": "<p>I think that @David's comment is worth noting, but the error tells you where the issue is:</p>\n<pre><code>“Atom” has no attribute “get_id”\n[snip]\nline 10, in <module> res_id = residue.get_id()[1]\n</code></pre>\n<p>Looking at the pymol <a href=\"https://pymolwiki.org/index.php/Get_... | null | {
"answers": [
{
"author": "Maximilian Press",
"author_url": "https://biology.stackexchange.com/users/22392/maximilian-press",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display... | [
{
"block_index": 0,
"code_text": "“Atom” has no attribute “get_id” File \"C:\\Users\\will\\AppData\\Local\\Schrodinger\\PyMOL2\\lib\\site-packages\\pmg_qt\\pymol_qt_gui.py\", line 1192, in file_run self.cmd.run(fname) File \"C:\\Users\\will\\AppData\\Local\\Schrodinger\\PyMOL2\\lib\\site-packages\\pymol\\pa... | CC BY-SA 4.0 | biology | null | 957edca1e016631c9828f39cfab756c303f69a19f7b5e58d4f29a8ca7c2dcd6a | SCT-7c7ce4a90a6f24620e19ac91 | null | null | Currently I am creating a selection that has residues 5 angstroms away from a ligand for alanine point mutations at each highlighted residue. Currently, the code is creating the specific selections I want, but it has the error below once selecting the model to use mutagenesis on. I specifically ran this code through Py... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:03:05.762873+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/04ed2aa314c5d877c5d79e42620d1f80988a21de8d429a22d8c6af5280cfd6e8_0... | <p>Currently I am creating a selection that has residues 5 angstroms away from a ligand for alanine point mutations at each highlighted residue. Currently, the code is creating the specific selections I want, but it has the error below once selecting the model to use mutagenesis on. I specifically ran this code through... | How to select residues in a selection for protein mutagenesis in PyMOL? | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://biology.stackexchange.com/questions/115427/how-to-select-residues-in-a-selection-for-protein-mutagenesis-in-pymol | train |
null | [
{
"answer_id": 117635,
"score": 1
}
] | [
{
"answer_html": "<p>As others are positing, that population is not in equilibrium. This is pretty obvious just from a cursory glance at the numbers. In no situation would you have a population at equilibrium where your Aa freqency is lower than both your AA frequency and your aa frequency. It's just mathematic... | null | {
"answers": [
{
"author": "rotaredom",
"author_url": "https://biology.stackexchange.com/users/24613/rotaredom",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display_name": "rotar... | [
{
"block_index": 0,
"code_text": "AA: 500\nAa: 200\naa: 300\n",
"post_id": 117607,
"sha256": "11a2c9e914d636e0f3d446805c176edbe0e40ca67d94b88c66c19036310d4759",
"source_url": "https://biology.stackexchange.com/questions/117607/is-this-population-not-in-hardy-weinberg-equilibrium"
},
{
"b... | CC BY-SA 4.0 | biology | null | 08d947ca011443c7883ff818d7123cf3c7a7bc52cd6207a4b343db67b96a3b87 | SCT-e1c99624e05dad58687dafd1 | null | null | I'm a biology student and, while answering a Hardy-Weinberg equilibrium exercise, I found an inconsistency. Could someone verify if my interpretation is correct?
Population data
Total individuals: 1000
Genotype counts:
AA: 500
Aa: 200
aa: 300
My analysis
Calculated allele frequency of a (q):
... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:03:01.208074+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/f1b387b48f36041f506f18fa9796531023c7eae1e796ec622ad56833c02bea98_0... | <p>I'm a biology student and, while answering a Hardy-Weinberg equilibrium exercise, I found an inconsistency. Could someone verify if my interpretation is correct?</p>
<h3>Population data</h3>
<p>Total individuals: 1000</p>
<p>Genotype counts:</p>
<pre><code>AA: 500
Aa: 200
aa: 300
</code></pre>
<h3>My analysis</h3>
<... | Is this population not in Hardy–Weinberg equilibrium? | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://biology.stackexchange.com/questions/117607/is-this-population-not-in-hardy-weinberg-equilibrium | train |
{
"answer_html": "<p>(a) Principal components absolutely can be used for making predictions. Principal component analysis (PCA) is not a prediction method itself, but you can compute principal components on your predictors and then use these to predict the response. This can be done in linear regression (principal co... | [
{
"answer_id": 676875,
"score": 6
},
{
"answer_id": 676876,
"score": 2
},
{
"answer_id": 676887,
"score": 5
},
{
"answer_id": 676896,
"score": 3
}
] | [
{
"answer_html": "<p>The principal components (PCs) are not guaranteed to be good predictors. PCA is performed without any regard for an outcome (<span class=\"math-container\">$y$</span>) variable and, alone, is not a prediction technique. However, the PCs most definitely can be entered into a regression model... | null | {
"answers": [
{
"author": "Dave",
"author_url": "https://stats.stackexchange.com/users/247274/dave",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display_name": "Dave",
... | [
{
"block_index": 0,
"code_text": "set.seed(2026)\n\n# Set up a regression\n#\nN <- 100\np <- N - 3\nX <- matrix(rnorm(N * p), N, p)\nB <- rbinom(p, 1, 0.3)\nEy <- X %*% B\ne <- rnorm(N, 0, 8)\ny <- Ey + e\n\n# Fit to all X variables using OLS\n#\nL_ols <- lm(y ~ X)\n\n# Calculate the MSE, considering the pa... | CC BY-SA 4.0 | statistics | null | 75289916dbeb3f563334247ca0a4653de359eb78d7360042c46047a4ea300f9f | SCT-30b8f67e6228f341e2fea696 | null | null | I've been reading up on pca. I've seen some people say that you cannot use pca for making predictions especially for linear regression models because it makes principal components that capture the most variability and not necessarily the actual relationships.
However, I've seen principal components be reverted to t... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:27:03.428408+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/774052c18cd9e8fa951e893347bfa3c1a85e743fc46ec4667572002cadb10cbf_1... | <p>I've been reading up on pca. I've seen some people say that you cannot use pca for making predictions especially for linear regression models because it makes principal components that capture the most variability and not necessarily the actual relationships.</p>
<p>However, I've seen principal components be reverte... | Can you use PCA to make predictions? are the issues with predictions specific to linear regression? | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://stats.stackexchange.com/questions/676874/can-you-use-pca-to-make-predictions-are-the-issues-with-predictions-specific-to | train |
{
"answer_html": "<p>As it turns out there is a mistake in Grogger and Carson's second derivatives of the zero-truncated negative binomial loglikelihood function wrt the regression parameters: blink and you'll miss it (the sign in one of the exponents should have been outside the parenthesis, probably a typo in the o... | [
{
"answer_id": 676993,
"score": 0
}
] | [
{
"answer_html": "<p>As it turns out there is a mistake in Grogger and Carson's second derivatives of the zero-truncated negative binomial loglikelihood function wrt the regression parameters: blink and you'll miss it (the sign in one of the exponents should have been outside the parenthesis, probably a typo in... | null | {
"answers": [
{
"author": "Ettore S",
"author_url": "https://stats.stackexchange.com/users/513606/ettore-s",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display_name": "Ettore S... | [
{
"block_index": 0,
"code_text": " n_regressors <- ncol(X)\n n_observ <- nrow(X)\n phi_sub <- (1 + p_theta*p_lambda)\n H_parent <- ((1 + p_theta*y) / phi_sub^2)*p_lambda\n if(zerotrunc){\n phi_sub_1a <- (1 / (phi_sub)^(1+p_alpha))\n phi <- (1/(1 + p_theta*p_lambda)^p_alpha) \n H_zt_A <- ( p_lambda*ph... | CC BY-SA 4.0 | statistics | null | dcc08cf0b10efb830071860d3f71e07b52ac03248dd10794ed1591cff2051ce8 | SCT-f4ee12e96468b941d56ccfc7 | null | null | In the context of zero-truncated negative binomial regression (see this other post (https://stats.stackexchange.com/q/676385/513606)) , twice-differentiating the loglikelihood yields a well-known result (see e.g., footnote 5 in this paper (https://www.jstor.org/stable/2096628)):
$$
\begin{aligned}
\frac{\partial^2... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:27:03.428408+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/774052c18cd9e8fa951e893347bfa3c1a85e743fc46ec4667572002cadb10cbf_1... | <p>In the context of zero-truncated negative binomial regression (see <a href="https://stats.stackexchange.com/q/676385/513606">this other post</a>) , twice-differentiating the loglikelihood yields a well-known result (<a href="https://www.jstor.org/stable/2096628" rel="nofollow noreferrer">see e.g., footnote 5 in this... | Hessian with invariably positive elements for zero-truncated negative binomial regression | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://stats.stackexchange.com/questions/676952/hessian-with-invariably-positive-elements-for-zero-truncated-negative-binomial-r | train |
{
"answer_html": "<p>You should think of compositions like this as really being for <span class=\"math-container\">$c + 1$</span> classes of things. You are thinking of sea grass cover as a univariate term, but it is really a 2-d space of the positive continuous values <span class=\"math-container\">$(0,1)^2$</span> ... | [
{
"answer_id": 676973,
"score": 1
}
] | [
{
"answer_html": "<p>You should think of compositions like this as really being for <span class=\"math-container\">$c + 1$</span> classes of things. You are thinking of sea grass cover as a univariate term, but it is really a 2-d space of the positive continuous values <span class=\"math-container\">$(0,1)^2$</... | null | {
"answers": [
{
"author": "Gavin Simpson",
"author_url": "https://stats.stackexchange.com/users/1390/gavin-simpson",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display_name": "... | [
{
"block_index": 0,
"code_text": "Count ~ All seagrass cover (%) + random site intercept\n",
"post_id": "stats:676971",
"sha256": "0fd0fc0c920f1110fc94144ab84ea05549367bf8f4d87ca0be2c46bdf101d14f",
"source_url": "https://stats.stackexchange.com/questions/676971/acceptable-conclusions-to-draw-fro... | CC BY-SA 4.0 | statistics | null | 37ebf4ccab8f61f92a8514f0cd49ce5b0eb1fb9803b4c1c45ea16b53f78655e0 | SCT-3e8ad75c79031fef967e9fe5 | null | null | What can be inferred about the relationship between an outcome and a predictor variable (in a statistical model) that is only a piece of compositional data?
I have submerged aquatic vegetation data (underwater seagrass, algae, etc.) by survey calendar year, month, day, and site (CYR_Keyfield). I want to model fish ... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:27:03.428408+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/774052c18cd9e8fa951e893347bfa3c1a85e743fc46ec4667572002cadb10cbf_1... | <p>What can be inferred about the relationship between an outcome and a predictor variable (in a statistical model) that is only a piece of compositional data?</p>
<p>I have submerged aquatic vegetation data (underwater seagrass, algae, etc.) by survey calendar year, month, day, and site (<code>CYR_Keyfield</code>). I ... | Acceptable conclusions to draw from incomplete compositional data as a model covariate? | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://stats.stackexchange.com/questions/676971/acceptable-conclusions-to-draw-from-incomplete-compositional-data-as-a-model-cov | train |
null | [
{
"answer_id": 677001,
"score": 6
}
] | [
{
"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which unfortunately may not "help"...</p>\n<p>F... | null | {
"answers": [
{
"author": "jginestet",
"author_url": "https://stats.stackexchange.com/users/380096/jginestet",
"content_license": "CC BY-SA 4.0",
"revision_attribution": [
{
"content_license": "CC BY-SA 4.0",
"contributor": {
"display_name": "jgines... | [
{
"block_index": 0,
"code_text": "data(summer_cat)\nputu <- summer_cat$NUS003\nGUESS <- ctmm.guess(putu,interactive=FALSE) # automated model guess\nM.OUF <- ctmm.fit(putu,GUESS)\nwAKDE <- akde(putu,M.OUF,weights=TRUE)\nAKDE <- akde(putu,M.OUF)\n\n#Plot\nEXT <- extent(AKDE,level=0.95) #DOP is missing. Assume... | CC BY-SA 4.0 | statistics | null | 34c8cf09713a56d442184f5e09ddd7f53aa41a01ed26ac35d4d292e5f6bcad3f | SCT-ca258033a99271411716e7c8 | [
"R"
] | null | I am doing a study on my university cats' home ranges. The tracker we are using is Damien Farine's Bluetooth Low-energy (BLE) beacons. The tags transmit a signal that can be detected by an Apple device. The phone receiving the signal captures a location from its own GPS and uploads the identity of the tag and its locat... | {
"original_provenance": {
"attribution_required": true,
"collected_at": "2026-10-01T03:27:03.428408+00:00",
"license": "CC BY-SA 4.0",
"license_url": "https://creativecommons.org/licenses/by-sa/4.0/",
"raw_file": "raw/codex_api_v1/774052c18cd9e8fa951e893347bfa3c1a85e743fc46ec4667572002cadb10cbf_1... | <p>I am doing a study on my university cats' home ranges. The tracker we are using is Damien Farine's Bluetooth Low-energy (BLE) beacons. The tags transmit a signal that can be detected by an Apple device. The phone receiving the signal captures a location from its own GPS and uploads the identity of the tag and its lo... | Asking for advice on cleaning GPS data from BLE trackers | question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inference | https://stats.stackexchange.com/questions/676997/asking-for-advice-on-cleaning-gps-data-from-ble-trackers | train |
{"answer_html":"<p>I see what's going on here. When <code>refit = F</code> DHARMa uses raw residuals(...TRUNCATED) | [
{
"answer_id": 677089,
"score": 5
}
] | [{"answer_html":"<p>I see what's going on here. When <code>refit = F</code> DHARMa uses raw residual(...TRUNCATED) | null | {"answers":[{"author":"Lukas Lohse","author_url":"https://stats.stackexchange.com/users/341520/lukas(...TRUNCATED) | [{"block_index":0,"code_text":"shiny_toad$fSeason <- as.factor(shiny_toad$fSeason)\nshiny_toad$fSite(...TRUNCATED) | CC BY-SA 4.0 | statistics | null | c4703328fafdb6b71f29af1e8463edc3575effa1b91d7fa371880b0b9a1ed7c4 | SCT-58d55cbca671fa1aae2f575a | null | [
"mgcv"
] | "Are long tails in a fitted vs. simulated residuals plot from DHARMa a sign of over-fitting (or any (...TRUNCATED) | {"original_provenance":{"attribution_required":true,"collected_at":"2026-10-01T03:27:03.428408+00:00(...TRUNCATED) | "<p>Are long tails in a fitted vs. simulated residuals plot from <code>DHARMa</code> a sign of over-(...TRUNCATED) | DHARMa: Are long tails in dispersion plot a problem? | "question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inferen(...TRUNCATED) | https://stats.stackexchange.com/questions/677079/dharma-are-long-tails-in-dispersion-plot-a-problem | train |
{"answer_html":"<p>The suspicion expressed in the comments is well-founded: your MLE function tends (...TRUNCATED) | [
{
"answer_id": 677117,
"score": 3
}
] | [{"answer_html":"<p>The suspicion expressed in the comments is well-founded: your MLE function tends(...TRUNCATED) | null | {"answers":[{"author":"jblood94","author_url":"https://stats.stackexchange.com/users/214015/jblood94(...TRUNCATED) | [{"block_index":0,"code_text":"library(MASS)\n\nz <- qnorm(0.975)\n\n## Simulate \nmu_true <- 0.5\nv(...TRUNCATED) | CC BY-SA 4.0 | statistics | null | 41398da8dbd1ebc9dcacb20a9bea5d02a6156e28f540d556ce602153656b8645 | SCT-edb642b23b7857840f0eca98 | null | null | "Here is a beta-binomial model (treated like repeated measures - $i$ is an index for each individual(...TRUNCATED) | {"original_provenance":{"attribution_required":true,"collected_at":"2026-10-01T03:27:03.428408+00:00(...TRUNCATED) | "<p>Here is a beta-binomial model (treated like repeated measures - <span class=\"math-container\">$(...TRUNCATED) | Can coverage probability decrease as sample size increases? | "question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inferen(...TRUNCATED) | https://stats.stackexchange.com/questions/677094/can-coverage-probability-decrease-as-sample-size-increases | train |
{"answer_html":"<p>You are using R version 4.6.1. Well, since R 4.6.0, from <a href=\"https://cran.r(...TRUNCATED) | [
{
"answer_id": 677136,
"score": 5
},
{
"answer_id": 677139,
"score": 16
},
{
"answer_id": 677141,
"score": 7
}
] | [{"answer_html":"<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noref(...TRUNCATED) | null | {"answers":[{"author":"Demetri Pananos","author_url":"https://stats.stackexchange.com/users/111259/d(...TRUNCATED) | [{"block_index":0,"code_text":"df <- structure(list(PreScore = c(16, 23, 17, 24, 22, 27, 20, 18, 21,(...TRUNCATED) | CC BY-SA 4.0 | statistics | null | 0104f45dbab9737f6409c77a6bc4a2e894b83ab23bd704361e79e00564d4ba71 | SCT-218133b3667b4e5daf2ee898 | null | null | "I ran wilcox.test(df$PostScore, df$PreScore, conf.int = TRUE, paired=TRUE, exact = TRUE) on the fol(...TRUNCATED) | {"original_provenance":{"attribution_required":true,"collected_at":"2026-10-01T03:27:03.428408+00:00(...TRUNCATED) | "<p>I ran <code>wilcox.test(df$PostScore, df$PreScore, conf.int = TRUE, paired=TRUE, exact = TRUE)</(...TRUNCATED) | Why does R not warn about ties in wilcox.test() | "question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inferen(...TRUNCATED) | https://stats.stackexchange.com/questions/677135/why-does-r-not-warn-about-ties-in-wilcox-test | train |
null | [
{
"answer_id": 677163,
"score": 2
},
{
"answer_id": 677165,
"score": 2
}
] | [{"answer_html":"<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_d(...TRUNCATED) | null | {"answers":[{"author":"EdM","author_url":"https://stats.stackexchange.com/users/28500/edm","content_(...TRUNCATED) | [{"block_index":0,"code_text":"mod_disp_constant: hs ~ thr + bd + (1 | obs), zi=~0, disp=~1\nmod_dis(...TRUNCATED) | CC BY-SA 4.0 | statistics | null | aacf292e175a320ca92325922c73fb91d823bb8253b9ee26523b0d8db5360fe9 | SCT-6db02b30844b68ff4b69f28a | [
"R"
] | null | "I need a mixed effects beta regression. I have used glmmTMB with beta distribution, logit link, to (...TRUNCATED) | {"original_provenance":{"attribution_required":true,"collected_at":"2026-10-01T03:27:03.428408+00:00(...TRUNCATED) | "<p>I need a mixed effects beta regression. I have used glmmTMB with beta distribution, logit link, (...TRUNCATED) | "glmmTMB with beta distribution for repeated measures with varying dispersion - Are p-values from di(...TRUNCATED) | "question troubleshooting lexeme plus literal pre block in question or answer; no root-cause inferen(...TRUNCATED) | https://stats.stackexchange.com/questions/677147/glmmtmb-with-beta-distribution-for-repeated-measures-with-varying-dispersion-a | train |
End of preview.
Scientific-Code-Troubleshooting
RegalFire — AI Data Foundry · Omar Soliman · ootiris@gmail.com
Problem
Real scientific troubleshooting with literal code and attributed community answers. 50 records in this source-derived release. Do not sum it with its public ancestors as unique examples.
Sources and provenance
SOURCE_LOCK.json pins approved releases and file hashes. Per-record source IDs and hashes link to source rows. Selected normalized native scientific-computing sources accompany the affected products in native_scicomp_source.jsonl; raw API scrape files are not included. Source URLs, author attribution and revision contributors are retained. Unverified Bionic inputs are excluded.
Schema and extraction
See schema.json for required fields/types. Nested source content and labels are independently validated. Question troubleshooting lexeme plus literal pre block. Language/tools require explicit mentions; missing error/method fields are null. No invented root causes, fixes or error messages.
What labels mean
Community answers, acceptance and scores are not scientific truth or executable success labels.
Validation and reproduction
Install Python 3.10+ dependencies: pip install jsonschema huggingface_hub. Run in the repository directory: python validator/validate.py . Pinned public ancestors require network or a populated HF cache; credentials are unnecessary. The validator does not import the builder. Source computational checkers were implemented independently from source generators. See validation_report.json, robustness_report.json and QA_REPORT.md.
Splits and leakage
{"holdout": 5, "test": 6, "train": 36, "validation": 3}. Scientific threads, normalized shared text/code and near questions (5-shingle Jaccard >=0.85) are globally grouped across the two scientific-derived products. Synthetic task families are kept in one split, with repeated source grid groups united. Source master seeds identify an entire generation run, not independent per-task seeds. These are template-held-out splits, not fresh-generation-seed experiments. Uneven splits are intentional. Public holdout labels are visible and are not secret or contamination-free. Exhaustive semantic near-duplicate detection is not claimed.
Intended uses and limitations
Research, regression and scoped private adaptation. No measured model performance, clients, ROI or clinical claims. Scientific code is not executed; community answers are not expert labels. Clinical flags and email-like content are excluded heuristically, not certified absent. Agent errors are controlled harness operations, not real-agent logs; counterfactual recovery is prediction correction, not physical intervention. Validation checks the specified contracts, not universal scientific correctness.
License
cc-by-sa-4.0. Preserve attribution, revision contributors and source notices. Scientific content remains CC BY-SA 4.0; MIT synthetic rows retain MIT notices. See THIRD_PARTY_LICENSES.md. No private exclusivity is promised for public source adaptations.
Custom / Private Dataset Work
RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.
Available:
- custom schemas
- private sources
- private holdout sets
- custom validators
- domain-specific hard cases
- continuous generation
- regression datasets
Contact: ootiris@gmail.com Hugging Face: RegalFire
Public license obligations remain applicable. Confidential private work requires client-owned or otherwise permitted sources.
Volume limitation
Approved inputs yield 50 explicit troubleshooting threads, below the requested 500. This is a bounded seed release without invented or repeated filler. Expansion requires further verified licensed raw input.
- Downloads last month
- 22