Find research datasets worth reusing

The number of sources limiting equality before the law during COVID-19

Rak Joanna, Bäcker Roman, The number of sources limiting equality before the law during COVID-19.   This dataset was elaborated for the research project Civil Disorder in Pandemic-ridden European Union. The latter was financially supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].

Number of sources on electoral law as a tool of democracy or autocracy

Rak Joanna, Bäcker Roman, Number of sources on electoral law as a tool of democracy or autocracy. This dataset was elaborated for the research project Civil Disorder in Pandemic-ridden European Union. The latter was financially supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].

Taking Over the Role of Law Enforcement by National Television: The Case Study of Women's Strike Protests in Pandemic-Ridden Poland. From 22.10. 2020 to 30.10 2020

Women’s strike protest during the second wave of the pandemic. Sources published on Tvp.info in the period from 22.10. 2020 to 30.10 2020. It was elaborated for the research project Civil Disorder in Pandemic-ridden European Union. The latter was financially supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].

Research on the Impact of Environmental Protection Tax Law on Corporate Technological Innovation: A Beijing-Tianjin-Hebei Regional Analysis

Qi Wang: 写作 - 原稿,形式分析。 Xin'e Li: 调查、写作 - 审查与编辑。 毛龙丹:调查,数据管理。 Meiling Yang:数据管理。

opencc-by-4.0Sep 2024View details →

Updated Data for: Food-washing monkeys recognize the law of diminishing returns

This is a new version draft of the data files for "Food washing monkeys recognize the law of diminishing returns" by Rosien et al.  The original reviewed pre-print was published on the elife website on 22 July 2024: https://elifesciences.org/reviewed-preprints/98520. The data stored here are for the updated version of record. The published text contains methods justifications and supporting citations. This dataset was revised based on the recommendations of three reviewers. It now contains: <ul> <li>two text files, to be run in the R programming environment (version of record is 4.4.1), containing code to replicate the GLMM analyses and produce the based figure files displayed in the paper.</li> <li>Two .csv files for running the GLMM statistics included in the revised text.</li> <li>One .csv file for figure 1, which contains sand geometric and compositional data.</li> <li>One .csv file containing intake rate data</li> <li>Six .csv files used to create figures 2 and S2 in the text.</li> <li>One Mathematica notebook file for producing the optimal cleaning model of figure 3. </li> </ul> A general note: when running the scripts, the file path you utilize will differ from the ones utilized in the current text, as it depends on where on one's computer the actual .csv files are stored. The "read.csv" command in the R code will need to be customized to a particular file path.

opencc-by-4.0Nov 2024View details →

NEW PERSPECTIVES IN CONTEMPORARY LAW

Purpose and Objectives: Law, over time, has faced all the changes in society, both political, economic and social, but also technological, the latter being a decisive factor of our days. It has permanently adapted itself through legislative changes at all levels, so that the very development of society is not hampered.  Proposals and Methodology: At present, decisive changes are taking place, due to the involvement of artificial intelligence, but also due to the fact that we are in the midst of a digital era, constantly promoted by the development of technology.  Results and Implications: The main issue that arises is the fact that it has not yet been established whether the legal norms, both national and international, can easily adapt to digitalization, so whether AI can use the norms as such in obtaining similar results to the judgments handed down by the courts.

dryad32/100

Raw images for: The narrowing of dendrite branches across nodes follows a well-defined scaling law

The systematic variation of diameters in branched networks has tantalized biologists since the discovery of da Vinci's rule for trees. Da Vinci's rule can be formulated as a power law with exponent two: the square of the mother branch's diameter is equal to the sum of the squares of those of the daughters. Power laws, with different exponents, have been proposed for branching in circulatory systems (Murray's law with exponent 3) and in neurons (Rall's law with exponent 3/2). The laws have been derived theoretically, based on optimality arguments, but, for the most part, have not been tested rigorously. Using super-resolution methods to measure the diameters of dendrites in highly branched Drosophila Class IV sensory neurons, we have found that these types of power laws do not hold. In their place, we have discovered a different diameter-scaling law: the cross-sectional area is proportional to the number of dendrite tips supported by the branch plus a constant, corresponding to a minimum diameter of the terminal dendrites. The area proportionality accords with a requirement for microtubules to transport materials and nutrients for dendrite tip growth. The minimum diameter may be set by the force, on the order of a few piconewtons, required to bend membrane into the highly curved surfaces of terminal dendrites. Because the observed scaling differs from Rall's law, we propose that cell biological constraints such as intracellular transport and protrusive forces generated by the cytoskeleton are important in determining the branched morphology of these cells.

opencc-zeroJun 2021View details →

FIGURES 37–39 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 37–39. Pseudosinella guanhaensis sp.nov.; 37, Abd IV chaetotaxy, left side; 38, tibiotarsi I; 39 empodial complex leg III.

FIGURES 30–34 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 30–34. Pseudosinella guanhaensis sp.nov.; 30, Details of sense organ of Ant. III with two type c, three type e and one type h sensilla, and surrounded by macrochaetae; 31, Details of sensilla at the ventro-lateral distal half Ant. II; 32, details of Ant. I dorso-laterally with 3 type h, 1 type i and 1 type a sensilla, and 2 h and 3 l; 33, ventro-lateral sensilla; 34, dorsal cephalic chaetotaxy.

FIGURES 35–36 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 35–36. Pseudosinella guanhaensis sp.nov.; 35, labial triangle chaetotaxy, left side; 36, trunk chaetotaxy, left side.

FIGURES 26–29 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 26–29. Pseudosinella guanhaensis sp.nov.; 26, antennal segment IV dorsal view, letters as in Fig. 17; 27, antennal segment III dorsal view, letter as in Fig. 17; 28, antennal segments I and II dorsal view, letters as in Fig. 17; 29, antennal segments I and II ventral view, letters as in Fig. 17.

FIGURES 21–25 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 21–25. Pseudosinella ambigua sp. nov.: 21, Abd IV dorsal chaetotaxy; 22, empodial complex leg III; 23, ventral tube anterior side (left); 24, Ventral tube posterior side (rigth); 25, mucro.

FIGURES 19–20 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 19–20. Pseudosinella ambigua sp. nov.: 19, labial triangle chaetotaxy, right side; 20, trunk chaetotaxy, right side.

FIGURES 11–12 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 11–12. Arrhopalites glabrofasciatus sp. nov.: 11, chaetotaxy of manubrium and dens; 12, mucro.

FIGURES 17–18 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 17–18. Pseudosinella ambigua sp. nov.: 17, antennal and body chaetae: a) subapical micro sense rod. b) sense type I. c) sense type II. d) sense type III. e) sense type IV. f) bristle-like. g) ciliated chaeta. h) microsensillum. i) micro sense organ. j) sense type V. l) sense type VI. m) fan-shaped chaeta type I. n) fan-shaped chaeta type II. o) micro or mesochaeta symbol. p) macrochaeta symbols. q) pseudopore symbols. r) bothriotrix. s) stout ciliated macrochaetae; 18, dorsal cephalic chaetotaxy.

FIGURES 5–7 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 5–7. Arrhopalites glabrofasciatus sp. nov.: 5, dorsal head chaetotaxy; 6, frontal head chaetotaxy; 7, clypeo-labral chaetotaxy.

FIGURES 2–4 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 2–4. Arrhopalites glabrofasciatus sp. nov.: 2, chaetotaxy of antennal segments I and II; 3, chaetotaxy of antennal segment III; 4, chaetotaxy of antennal segment IV, with apical bulb at the base of a hooklike chaeta.

FIGURES 8–10 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation

FIGURES 8–10. Arrhopalites glabrofasciatus sp. nov.: 8, chaetotaxy of foreleg; 9, chaetotaxy of mid leg; 10, chaetotaxy of hind leg.

opencc-by-4.0May 2024View details →

Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules

<div> </div> <h3>Data</h3>  This folder contains the raw data used during this work. `out_seq_total.txt` contains information on the sequences used (mutations, number of mutations, etc.). `output_energies_total.txt` contains the response variables, which include: a) unrelaxed EvoEF energies (peptides); b) relaxed EvoEF energies (peptides, `*_repaired.txt`); and c) solvation energies (molecules). 3D structures are provided in `.xyz` format in the subfolder `XYZ` (molecules). <div> <div>GB1 dataset</div> <div>The GB1 dataset was not generated by us (https://doi.org/10.48550/arXiv.2405.05167). If you use the GB1 dataset, please cite the original paper:</div> <div>Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., & Sun, R. (2016). Adaptation in protein fitness landscapes is facilitated by indirect paths. eLife, 5:e16965. doi:10.7554/eLife.16965</div> </div> <h3>Results</h3> This folder contains the results (outputs) of the ML models trained using the provided scripts (see github repository). Such results are incuded in the form of `.npy` files. To load the files please include the option `allow_pickle=True`.  Each `.npy` file contains the following keys:  * `initial_parameters`: script inputs.  * `d_encoder`: encoder used (not always included).  * `ns_train`: number of training points used for the LCs (rounded, integers).  * `ns_train_float`: number of training points used for the LCs (not rounded, float).  * `ns_train_norm`: number of training points used for the LCs (normalized, float).  * `res`: test MAEs.  * `res_tot`: (train,validation,test) MAEs.  * `res_tot_mut`: (train,validation,test) MAEs sorted by mutation number.  * `l_opt`: optimal kernel length used during the test.  * `ls`: kernel lengths used for grid search.  * `idx_seeds`: indices used to reshuffle the data. If one want to rebild the initial order use `np.argsort(idx_seeds)`.  * `alpha_opt`: optimal regression parameters used to calculate the test error. To sort the data use `alpha_opt[i][ii][np.argsort(idx_seeds[ii,0:arg_train_max].astype(int)[:ns_train[i]]`. Where `i` is the replicate number (0-99) and ii is the idex in the LC.  * `valid_errs`: validation error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.  * `test_errs`: test error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.