cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vjpj6278cn.1
title: A Systematic Review of Literature on the Burden of Ancillary Tasks on Teachers

description: This is a systematic review of literature on the Burden of Ancillary Tasks on Teachers. Teachers have many roles beyond just teaching. In the classroom, they often act as second parents, caregivers, counselors, entertainers, motivators, and sometimes even detectives. This study used a research methodology known as a Systematic Literature Review, with the objective of identifying the adverse consequences of a teacher's workload or ancillary tasks. The researcher utilize primary data base search engines such as Scopus, Science Direct, PubMed, ERIC, and Google Scholar. The selection process followed the PRISMA approach. The researcher conducted a comprehensive literature review on ancillary tasks to understand their impact on teachers' well-being, professional development, and the educational environment. This study seeks to help educators recognize that a heavy workload may lead to various negative or positive impacts on their well-being. This study aimed to review teachers' experiences with ancillary functions. The findings indicate that teachers view these additional responsibilities as extra burdens on their primary role of teaching, even though they present opportunities for personal and professional growth. By taking on these roles, teachers may have to sacrifice time for instruction and with their loved ones. This unfortunate situation can be alleviated, if not entirely prevented, through effective workload management. Therefore, it is essential to seek valuable insights from more experienced and knowledgeable individuals to ensure that tasks are completed efficiently and effectively. Based on the findings of the study, the researcher offers the following recommendations: (1) Teachers should create a checklist to prioritize tasks by importance and urgency, enabling them to efficiently manage their roles as both educators and coordinators; Given the limited number of teaching staff, the school head should distribute additional assignments fairly among teachers; The division should provide orientation for newly appointed coordinators to guide them in their responsibilities; and The Department of Education should assess the workload assigned to teachers. An evidence-based and systematic approach is essential to reduce teachers' workload. Conducting a thorough time-use study can clearly identify which tasks should be assigned to teachers and which ones should be removed from their responsibilities. Therefore, the results of this thorough analysis are expected to provide a basis for future research, especially in the pursuit of effective solutions and interventions to alleviate the increasing negative impacts of workload on teachers.
DOI: 10.7910/dvn/1fuz0s/0j6jsc
title: eps_chic_n16u075tp00_ndk064_v2_p=0.17.dat
DOI: 10.7910/dvn/1fuz0s/8ris0o
title: eps_cond_vs_T_n16u075tp00_ndk064_v2_p=0.17.dat
DOI: 10.7910/dvn/1fuz0s/gng4yx
title: eps_cond_vs_T_n16u075tp00_ndk064_v2_p=0.15.dat
DOI: 10.7910/dvn/1fuz0s/sx2b1i
title: eps_chic_n16u075tp00_ndk064_v2_p=0.20.dat
DOI: 10.7910/dvn/1fuz0s/wfvgf4
title: eps_cond_vs_T_n16u075tp00_ndk064_v2_p=0.20.dat
DOI: 10.7910/dvn/1fuz0s/3rd8wx
title: eps_tau_vs_T_n16u075tp00_ndk064_v2_p=0.17.dat
DOI: 10.7910/dvn/1fuz0s/81fmov
title: eps_tau_vs_T_n16u075tp00_ndk064_v2_p=0.15.dat
DOI: 10.7910/dvn/1fuz0s/a3cd9k
title: eps_sigma_vs_om_n14u075tp00_ndk064_p=0.15, T=4.00.dat
DOI: 10.7910/dvn/1fuz0s/lysj9o
title: eps_sigma_vs_om_n14u075tp00_ndk064_p=0.15, T=2.03.dat
DOI: 10.7910/dvn/1fuz0s/m4yfp8
title: eps_sigma_vs_om_n14u075tp00_ndk064_p=0.15, T=3.01.dat
DOI: 10.7910/dvn/1fuz0s/pbhiqo
title: eps_sigma_vs_om_n14u075tp00_ndk064_p=0.15, T=0.99.dat
DOI: 10.7910/dvn/1fuz0s/drfh3l
title: examine_set.m
DOI: 10.7910/dvn/1fuz0s/e9o6wg
title: exp1D.m
DOI: 10.7910/dvn/1fuz0s/k20iqq
title: errorbarxy.m
DOI: 10.7910/dvn/1fuz0s/knzu0h
title: EulerAngles.m
DOI: 10.7910/dvn/1fuz0s/sp39nh
title: eps_tau_vs_T_n16u075tp00_ndk064_v2_p=0.20.dat
DOI: 10.7910/dvn/lbo9lj/giu3wl
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/1hegrq
title: expt_hydro_fit_bootstrap_fromfitpts_summary_natural_units.txt
DOI: 10.7910/dvn/1fuz0s/lrtjpj
title: export_avg_2d_images.m
DOI: 10.7910/dvn/1fuz0s/m9w0h8
title: expt_compressibility_n=0.80.txt
DOI: 10.7910/dvn/1fuz0s/q1nt0h
title: Extract_Data_From_Fig.m
DOI: 10.7910/dvn/1fuz0s/ugaewr
title: expt_ones_conductivity_natural_units.txt
DOI: 10.7910/dvn/1fuz0s/fncqdt
title: find_duplicate_fnames.m
DOI: 10.7910/dvn/1fuz0s/scgx6z
title: Fermi1DModConf.m
DOI: 10.7910/dvn/1fuz0s/tggp7u
title: FermiFit1D.m
DOI: 10.7910/dvn/1fuz0s/toa2nb
title: FindAngle.m
DOI: 10.7910/dvn/1fuz0s/uxuam9
title: findFileGeneral.m
DOI: 10.7910/dvn/1fuz0s/yprbbp
title: Fermi1DGauss.m
DOI: 10.7910/dvn/ceaf5l/xrynjq
title: petitions.xlsx
DOI: 10.17632/w9kvt4b9w7.1
title: Analysis of the main compounds of the essential oil of mastic leaves (Schinus terebinthifolius Raddi) for the treatment of inflammatory diseases: phytochemical identification and in silico study.

description: There is a constant search for alternatives for the treatment of inflammatory diseases, therefore the use of in silico studies reduces costs and time in research for the treatment of different types of inflammatory diseases that can be treated by inhibiting the enzymes cyclooxygenases 1 and 2. The objective This study was to identify the compounds present in the essential oil of Schinus terebinthifolius Raddi leaves, as well as the in silico study of the anti-inflammatory action of its main compounds for the treatment of inflammation. The oil (OE) was obtained by hydrodistillation, the constituents identified by gas chromatography coupled to mass spectrometry. 10 major compounds were identified, submitted to in silico pharmacokinetic and toxicological prediction studies (ADMET) on the Swissadme platform, where they showed good results. In molecular docking and redocking, good binding energy values were obtained in Autodock Vina (AGbind kcal/mol) of -8.781 for Murolene with Cox1 and camphorne with -9.356 as the lowest value for Cox2. It was observed that caryophyllene oxide was the one that presented the most distinct values among the Cox isoforms. Hydrophobic interactions and some interactions due to hydrogen bonds were observed. It is suggested that caryophyllene oxide is of greater interest, as it inhibits Cox1 less and Cox2 more.
DOI: 10.7910/dvn/1fuz0s/hxleki
title: gaussian1D.m
DOI: 10.7910/dvn/1fuz0s/iuqhx0
title: fit2D.m
DOI: 10.7910/dvn/1fuz0s/kbfmpv
title: fits2AIA.m
DOI: 10.7910/dvn/1fuz0s/kitjel
title: fit1D.m
DOI: 10.7910/dvn/1fuz0s/thvvix
title: fit_model_simultaneous.m
DOI: 10.7910/dvn/1fuz0s/7lzkoi
title: gaussianBeamWaist1D.m
DOI: 10.7910/dvn/1fuz0s/8yl7ts
title: get_color_map.m
DOI: 10.7910/dvn/1fuz0s/cz027q
title: gaussian2D.m
DOI: 10.7910/dvn/1fuz0s/eh5pbx
title: getBadFitsFiles.m
DOI: 10.7910/dvn/1fuz0s/ew73sp
title: getCenterMass.m
DOI: 10.7910/dvn/1fuz0s/zwpxdp
title: getAIAStyleTimeStamp.m
DOI: 10.7910/dvn/lrdfdv/mqrejf
title: petitions.xlsx
DOI: 10.17632/4b2gdbv6zg.1
title: Whistle Blowers in Schools: Dissidents or Reformers

description: Whistleblowing has traditionally been viewed as detrimental to organizational interests. Managers, on the other hand, can boost their organization's performance and efficiency by encouraging whistling. Though whistleblowing was not widely accepted as a true statement, members of organizations, particularly public employees, were unable to reveal wrongdoings for years in school due to the possibility of reprisal for blowing the whistle (Park & Lewis 2018).
Whistleblowing has historically been regarded as damaging to organizational interests. Managers, on the other hand, can improve the performance and efficiency of their organization by encouraging whistling. Though whistleblowing was not commonly regarded as a truth, members of organizations, particularly public employees, were unable to disclose wrongdoings for years in school due to the risk of retaliation for blowing the whistle (Fredin, Venkatesh, Riley, & Eldridge 2018).
Whistleblowers can be reformers as well as dissenters. Whistleblowers merely carry out their jobs, which they regard as requiring them to report problems as soon as they are identified. They resist the real power structure because they defend the organization's officially declared policies as well as alternative moral or professional standards. These whistleblowers are frequently caught off guard and badly harmed by retaliation. People who are purposefully contrarian frequently forecast the outcome better (Pillay, Ramphul, Dorasamy & Meyer 2018).Mehrotra, Mishra, Srikanth, Tiwari, and Kumar (2020) reported that as a result of lawsuits, over 90% of whistleblowers were blackballed, forced to leave their positions, or lost their life savings. Furthermore, in many nations, whistleblowing is frowned upon. Employees are the most effective parties in limiting the occurrence of wrongdoing in firms, and one of the responses that they exhibit in response to wrongdoing is whistleblowing. Witnesses to any crime, however, may choose not to report it for fear of retaliation.
Using whistleblowers to uncover infractions is a successful technique. Whistleblowing can help create greater openness, accountability, and good governance (Previtali & Cerchiello 2018). However, recent research indicates that filing a whistleblower complaint can be difficult. Whistleblowers frequently face unfavorable consequences like as promotion, termination, and safety concerns. However, even though whistleblowing is critical for aiding school change and is generally perceived as consistent with serving the public or society at large, there is a persisting gap in the factors that impact whistleblowing in government institutions, particularly in schools. As a result, the researcher is curious to learn whether teachers in the school are reformers or dissidents when it comes to whistleblowing.
DOI: 10.7910/dvn/1fuz0s/b9cnm5
title: getImageSize.m
DOI: 10.7910/dvn/1fuz0s/bkp79l
title: getFolderAvg.m
DOI: 10.7910/dvn/1fuz0s/orgkkp
title: getFolderPics.m
DOI: 10.7910/dvn/1fuz0s/zfjqtb
title: getDataSetPath.m
DOI: 10.7910/dvn/1fuz0s/fpz6uv
title: get_moment.m
DOI: 10.7910/dvn/1fuz0s/mm1aaw
title: getNormOD.m
DOI: 10.7910/dvn/1fuz0s/o6tlfi
title: getLogStats.m
DOI: 10.7910/dvn/1fuz0s/vf6nsl
title: getOD_Alpha.m
DOI: 10.7910/dvn/1fuz0s/ylpkna
title: getLogTopLabels.m
DOI: 10.7910/dvn/1fuz0s/ylyvet
title: getOD.m
DOI: 10.7910/dvn/1fuz0s/8qdc58
title: getShiftedMat.m
DOI: 10.7910/dvn/1fuz0s/dnjpuh
title: getROI.m
DOI: 10.7910/dvn/1fuz0s/njtjny
title: guessCenter.m
DOI: 10.7910/dvn/1fuz0s/pavabt
title: get_RIN.m
DOI: 10.7910/dvn/1fuz0s/wmqmsl
title: getOtherDataPath.m
DOI: 10.17632/tn7hwttbcc.1
title: The Behavioral Intention of College Students Majoring in Dance in Shandong Province to Adopt Chinese Folk Dance Online Courses

description: Research Hypotheses
This study aimed to investigate the factors that influence students' behavioral intentions to adopt online learning for Chinese folk dance courses. The research hypotheses were formulated based on the Theory of Planned Behavior (TPB), focusing on the following relationships:
H1: Students' attitudes (AA) positively influence their behavioral intention (DI) to adopt an online learning platform.
H2: Subjective norms (BS) positively influence students' selection of online learning for Chinese folk dance courses.
H3: Perceived behavioral control (CP) positively influences students' behavioral intention to engage in online learning.
Data Findings
The data collected from 338 students were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The key findings include:
Subjective norms had the strongest effect on behavioral intention with a path coefficient of 0.384. This suggests that students are heavily influenced by the expectations and opinions of peers and faculty in their decision to adopt online learning.
Perceived behavioral control also had a significant impact, with a path coefficient of 0.339. This indicates that students’ confidence in their ability to navigate online platforms and succeed in online courses plays a major role in their intention to engage in online learning.
Attitude had the weakest relationship with behavioral intention, with a path coefficient of 0.189. While attitude does influence students’ decisions, it plays a relatively smaller role compared to subjective norms and perceived behavioral control.
Data Collection and Use
The study collected data through an electronic questionnaire, distributed via email and WeChat groups to students majoring in dance in several higher education institutions across Shandong Province. The questionnaire was designed based on the TPB framework, and contained 17 items measuring attitude, subjective norms, perceived behavioral control, and behavioral intention. A seven-point Likert scale was used, with participants selecting a number from 1 (strongly disagree) to 7 (strongly agree) to reflect their agreement with each statement.
After screening for missing data and incomplete responses, 338 valid responses were included in the analysis. Statistical analyses were performed using SPSS version 29.0 and Smart-PLS version 4.0, allowing for the evaluation of the relationships between the constructs and the overall model fit.
Data Interpretation
The data were interpreted using path coefficients from the PLS-SEM analysis. The results indicate that subjective norms and perceived behavioral control are key predictors of behavioral intention, while attitude plays a smaller, albeit still important, role. These findings suggest that the success of online learning platforms—especially for Chinese folk dance courses—depends largely on how supported students feel by their peers and educators, as well as their confidence in navigating the technological environment.
DOI: 10.7910/dvn/1fuz0s/awedsg
title: img2obj_coord.m
DOI: 10.7910/dvn/1fuz0s/gmlziu
title: latt2img_coord.m
DOI: 10.7910/dvn/1fuz0s/ndxaxj
title: img2latt_coord.m
DOI: 10.7910/dvn/1fuz0s/urwitk
title: latticeFGRadial1D.m
DOI: 10.7910/dvn/1fuz0s/ynhtfg
title: img2roi_coord.m
DOI: 10.7910/dvn/q3gakj/aejw7f
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/2ronuy
title: latticeFGRadial_TwoAxialBands1D.m
DOI: 10.7910/dvn/1fuz0s/c2tbjl
title: latticeFGRadial_NNCorr1D.m
DOI: 10.7910/dvn/1fuz0s/crozey
title: latticeFGRadial_NNCorr_VaryingLatticeDepth1D.m
DOI: 10.7910/dvn/1fuz0s/l8rrxr
title: latticeFGRadial_NNCorr_GaussPot1D.m
DOI: 10.7910/dvn/1fuz0s/rkgwlp
title: latticeFGRadial_GaussPot1D.m
DOI: 10.7910/dvn/1fuz0s/2wf9ex
title: lorentzian1D.m
DOI: 10.7910/dvn/1fuz0s/bpgngy
title: lsq_fixedp.m
DOI: 10.7910/dvn/1fuz0s/hhhgkt
title: latticeFGRadial_VaryingLatticeDepth1D.m
DOI: 10.7910/dvn/1fuz0s/n24lpw
title: line1D.m
DOI: 10.7910/dvn/1fuz0s/wavdfo
title: linearity_params_fits.dat
DOI: 10.7910/dvn/1fuz0s/yyxstl
title: linearity_params_vs_amp.dat
DOI: 10.7910/dvn/1fuz0s/9hd6j8
title: maskAngRegion.m
DOI: 10.7910/dvn/1fuz0s/cdeet6
title: mask2D.m
DOI: 10.7910/dvn/1fuz0s/e10ljr
title: mask2d_2.m
DOI: 10.7910/dvn/1fuz0s/ft7wtc
title: non_int_fg_compress_n=0.4125.dat
DOI: 10.7910/dvn/1fuz0s/m0uvtr
title: normalizeStripes.m
DOI: 10.7910/dvn/2rtw47/wdyiv6
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/okvprg
title: obj2img_coord.m
DOI: 10.7910/dvn/1fuz0s/q1wfvi
title: plot_hydro_params.m
DOI: 10.7910/dvn/1fuz0s/ssicjj
title: path2dataset.m
DOI: 10.7910/dvn/1fuz0s/wopa1c
title: plot_ks_gammas.m
DOI: 10.7910/dvn/1fuz0s/ws2opq
title: parseLog.m
DOI: 10.7910/dvn/1fuz0s/yhu9uw
title: parabola1D.m
DOI: 10.17632/3d2r8gxmnr.1
title: Distortion classification and Hotspot recognition dataset

description: The first column of the sample is wavelength, the second column is reflectance, and the third column is category. In the hotspot recognition dataset, category "0" represents no hotspot occurrence, while category "1" represents hotspot occurrence. In the distortion recognition dataset, category "1" represents "normal spectrum", category "2" represents "broadened distortion spectrum", category "3" represents "asymmetric distortion spectrum", and category "4" represents "broadened and asymmetric distortion spectrum"
DOI: 10.7910/dvn/1fuz0s/0ctd28
title: PlotModelParams.m
DOI: 10.7910/dvn/1fuz0s/1ykumf
title: PolyLog2D.m
DOI: 10.7910/dvn/1fuz0s/2d43bz
title: readimg.m
DOI: 10.7910/dvn/1fuz0s/v2zgq7
title: plot_other_dat.m
DOI: 10.7910/dvn/1fuz0s/wpypup
title: PlotModelParams_v2.m
DOI: 10.7910/dvn/lgnede/nx7fjp
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/ecufe1
title: run_analysis.m
DOI: 10.7910/dvn/1fuz0s/gmdntw
title: roi_ellipse_dat_2017-11-01_Folders=047-060.txt
DOI: 10.7910/dvn/1fuz0s/prvkt8
title: roi2img_coord.m
DOI: 10.7910/dvn/1fuz0s/ty84rv
title: resizePics.m
DOI: 10.7910/dvn/1fuz0s/wyjav4
title: rf3DSpectra.m
DOI: 10.7910/dvn/1fuz0s/z8uca1
title: rf2DSpectra.m
DOI: 10.7910/dvn/1fuz0s/2oj5tv
title: Semiclassical_Density_In_Lattice.m
DOI: 10.7910/dvn/1fuz0s/8f2sja
title: saveAIA.m
DOI: 10.7910/dvn/1fuz0s/h1q8d4
title: save_data_file.m
DOI: 10.7910/dvn/1fuz0s/rxxwfr
title: saveFits.m
DOI: 10.7910/dvn/1fuz0s/vjcz12
title: set17_Jan.m
DOI: 10.7910/dvn/xvsu7e/ttfmhi
title: petitions.xlsx
DOI: 10.17632/vfz5tphnfh.1
title: Gray Scale Synthetic Images for Edge Preservation Analysis

description: Image type : Gray Scale 
Size 256x256
PNG & JPG

Types of Variation : 

# Variation 1: Add Gaussian Noise
# Variation 2: Add Salt and Pepper Noise
# Variation 3: Apply Gaussian Blur
# Variation 4: Apply Bilateral Filter
# Variation 5: Compression Artifacts

Levels of variations for each base image = 11 

3 levels of noise (low, medium, high).
3 levels of blurring.
3 levels of edge-preserving filtering.
2 levels of compression.

Total images: 300 base images × 11 variations
DOI: 10.7910/dvn/1fuz0s/24nm0a
title: set18_Jan.m
DOI: 10.7910/dvn/1fuz0s/fiaa4v
title: set1_Nov1.m
DOI: 10.7910/dvn/1fuz0s/kuvhcj
title: set1_oct16.m
DOI: 10.7910/dvn/1fuz0s/llbvot
title: set1_Nov7.m
DOI: 10.7910/dvn/1fuz0s/puyzga
title: set19_Jan.m
DOI: 10.7910/dvn/1fuz0s/xn1eaq
title: set1_oct19.m
DOI: 10.7910/dvn/1fuz0s/3femht
title: set2_Nov1.m
DOI: 10.7910/dvn/1fuz0s/4qvp1q
title: set2_Nov7.m
DOI: 10.7910/dvn/1fuz0s/5mk9nd
title: set2_oct19.m
DOI: 10.7910/dvn/1fuz0s/9wgviu
title: set2_oct16.m
DOI: 10.7910/dvn/1fuz0s/ln74og
title: set1_Oct23.m
DOI: 10.7910/dvn/1fuz0s/sxgzy1
title: set2_Oct23.m
DOI: 10.7910/dvn/4yxqje/nkvzq8
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/akxu34
title: set2_sept27.m
DOI: 10.7910/dvn/1fuz0s/ccqs0z
title: set3_oct19.m
DOI: 10.7910/dvn/1fuz0s/lsv78c
title: set3_oct16.m
DOI: 10.7910/dvn/1fuz0s/pvasex
title: set3_Nov1.m
DOI: 10.7910/dvn/1fuz0s/vd78vf
title: set3_Nov7.m
DOI: 10.7910/dvn/1fuz0s/0pbopk
title: set4_oct16.m
DOI: 10.7910/dvn/1fuz0s/bc8sy2
title: set3_Oct23.m
DOI: 10.7910/dvn/1fuz0s/kpluhy
title: set4_Nov7.m
DOI: 10.7910/dvn/1fuz0s/lhv1yj
title: set4_Nov1.m
DOI: 10.7910/dvn/1fuz0s/wsnpfo
title: set4_Oct23.m
DOI: 10.7910/dvn/1fuz0s/yrzona
title: set4_oct19.m
DOI: 10.7910/dvn/wpfvjn/hcop2i
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/befo87
title: set5_Jan.m
DOI: 10.7910/dvn/1fuz0s/egzflr
title: set5_Nov1.m
DOI: 10.7910/dvn/1fuz0s/evijmo
title: set5_oct16.m
DOI: 10.7910/dvn/1fuz0s/mb7pje
title: set5_Nov7.m
DOI: 10.7910/dvn/1fuz0s/vrzev1
title: set5_Oct23.m
DOI: 10.7910/dvn/1fuz0s/41nags
title: set6_Jan.m
DOI: 10.7910/dvn/1fuz0s/gogvsu
title: set9_Jan.m
DOI: 10.7910/dvn/1fuz0s/pxtycu
title: showMasks.m
DOI: 10.7910/dvn/1fuz0s/rlfqwn
title: set8_Jan.m
DOI: 10.7910/dvn/1fuz0s/ugxuix
title: showAzAvgBins.m
DOI: 10.7910/dvn/hfpvrc/1qfy5s
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/cqcn5n
title: test_damped_osc_fn.m
DOI: 10.7910/dvn/1fuz0s/odegeo
title: sinusoid1D.m
DOI: 10.7910/dvn/1fuz0s/sddcul
title: testplot.m
DOI: 10.7910/dvn/1fuz0s/vapso4
title: test_2D_gaussian_fn.m
DOI: 10.7910/dvn/1fuz0s/vkfsm7
title: TEMPLATE.m
DOI: 10.7910/dvn/1fuz0s/0tsdj9
title: TFGaussian1DAzAvg.m
DOI: 10.7910/dvn/1fuz0s/9o71o1
title: test_transform.m
DOI: 10.7910/dvn/1fuz0s/kmjggq
title: TestRecTransform.m
DOI: 10.7910/dvn/1fuz0s/oaazia
title: thomasFermi1D.m
DOI: 10.7910/dvn/1fuz0s/qbmw1c
title: thomasFermi2D.m
DOI: 10.7910/dvn/q4dqzp/aht7fn
title: petitions.xlsx
DOI: 10.7910/dvn/1fuz0s/ge4ysy
title: thomasFermiPlusGaussian1D.m
DOI: 10.7910/dvn/1fuz0s/gq8fr8
title: timestamp2dataset.m
DOI: 10.7910/dvn/1fuz0s/jclce1
title: timeStampFitsFile.m
DOI: 10.7910/dvn/1fuz0s/k7t9zk
title: transformImg2Obj.m
DOI: 10.7910/dvn/1fuz0s/ro4tm0
title: tilefigs.m
DOI: 10.7910/dvn/1fuz0s/t5avch
title: transformObj2Img.m
DOI: 10.7910/dvn/1fuz0s/9i6b40
title: vpp2psd.m
DOI: 10.7910/dvn/1fuz0s/bo42bl
title: vpp2dBm.m
DOI: 10.7910/dvn/1fuz0s/fh26rq
title: watchReconstruction.m
DOI: 10.7910/dvn/1fuz0s/gqizb0
title: vrms2dBm.m
DOI: 10.7910/dvn/1fuz0s/uohzld
title: vectorMag1D.m
DOI: 10.7910/dvn/1fuz0s/fl3oax
title: writeLog.m
DOI: 10.7910/dvn/3ocaln/h1rqir
title: petitions.xlsx
DOI: 10.7910/dvn/wmakbw/3qc2b9
title: petitions.xlsx
DOI: 10.7910/dvn/saugxg/pvbwsm
title: petitions.xlsx
DOI: 10.7910/dvn/z79iy6/lyo6bi
title: petitions.xlsx
DOI: 10.7910/dvn/74887p/9rvjms
title: petitions.xlsx
DOI: 10.7910/dvn/pwtwt6/nyft6w
title: petitions.xlsx
DOI: 10.7910/dvn/yucfsx/kz0wiv
title: petitions.xlsx
DOI: 10.7910/dvn/nqnylf/2kqade
title: petitions.xlsx
DOI: 10.7910/dvn/xzpaic/6l96tm
title: petitions.xlsx
DOI: 10.7910/dvn/amnoth/rl7lax
title: petitions.xlsx
DOI: 10.7910/dvn/sigimz/m7sacp
title: petitions.xlsx
DOI: 10.7910/dvn/iupqaz/xsetpv
title: petitions.xlsx
DOI: 10.7910/dvn/tkwwcz/ueczkg
title: petitions.xlsx
DOI: 10.7910/dvn/xb1eoa/ukrhr5
title: petitions.xlsx
DOI: 10.7910/dvn/cqdzhw/gaxn6y
title: petitions.xlsx
DOI: 10.15468/dl.qqwfeg
title: Occurrence Download

description: A dataset containing 14440 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Prosopis juliflora (Sw.) DC.",
 "Year 2000-2023"
 ]
}

 The dataset includes 14440 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029033-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/ukonly/gwkuqb
title: petitions.xlsx
DOI: 10.15468/dl.r56utc
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Canis aureus Linnaeus, 1758"
 ]
}

 The dataset's 4 records were derived from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028994-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/c1jdkh/euej0m
title: petitions.xlsx
DOI: 10.7910/dvn/y2odvp/xwsy51
title: petitions.xlsx
DOI: 10.15468/dl.39v265
title: Occurrence Download

description: A dataset containing 28085 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"2706056","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 28085 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029032-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/uv5iqj/aftu3h
title: petitions.xlsx
DOI: 10.7910/dvn/qumvua/qsvw1b
title: petitions.xlsx
DOI: 10.7910/dvn/ugf8ha/5213f0
title: petitions.xlsx
DOI: 10.7910/dvn/lcbqkk/jkozyy
title: petitions.xlsx
DOI: 10.7910/dvn/peqtcn/wogzd0
title: petitions.xlsx