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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    BadZipFile
Message:      zipfiles that span multiple disks are not supported
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1492, in compute_config_parquet_and_info_response
                  fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 683, in fill_builder_info
                  ) = retry_validate_get_features_num_examples_size_and_compression_ratio(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 602, in retry_validate_get_features_num_examples_size_and_compression_ratio
                  validate(pf)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 640, in validate
                  raise TooBigRowGroupsError(
              worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 389851202 which exceeds the limit of 300000000
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
                  for item in generator(*args, **kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 84, in _generate_tables
                  for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1577, in __iter__
                  for x in self.generator(*self.args, **self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1658, in _iter_from_urlpaths
                  if xisfile(urlpath, download_config=download_config):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1024, in xisfile
                  fs, *_ = url_to_fs(path, **storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 395, in url_to_fs
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 293, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 80, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "<string>", line 3, in __init__
                File "/usr/local/lib/python3.9/unittest/mock.py", line 1092, in __call__
                  return self._mock_call(*args, **kwargs)
                File "/usr/local/lib/python3.9/unittest/mock.py", line 1096, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                File "/usr/local/lib/python3.9/unittest/mock.py", line 1157, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 934, in track_metadata_read_once
                  out = func(instance, fo=urlpath, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 62, in __init__
                  self.zip = zipfile.ZipFile(
                File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
                  endrec = _EndRecData(fp)
                File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
                  return _EndRecData64(fpin, -sizeEndCentDir, endrec)
                File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
                  raise BadZipFile("zipfiles that span multiple disks are not supported")
              zipfile.BadZipFile: zipfiles that span multiple disks are not supported
              
              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 1505, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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image_filename
string
caption
string
dataset/figures/0704_0008_pgascmp1.eps
Principal isentrope and shock Hugoniot for air (perfect gas): numerical calculations for general material models, compared with analytic solutions.
dataset/figures/0704_0008_AlcmpT.eps
Shock Hugoniot for Al in pressure-temperature space, for different representations of the equation of state.
dataset/figures/0704_0008_Becmpvp.eps
Principal adiabat and shock Hugoniot for Be in normal stress-compression space, neglecting strength (dashed), for Steinberg-Guinan strength (solid), and for elastic-perfectly plastic with Y=10\,GPa (dotted).
dataset/figures/0704_0008_Becmppus.eps
Principal adiabat and shock Hugoniot for Be in shock speed-normal stress space, neglecting strength (dashed), for Steinberg-Guinan strength (solid), and for elastic-perfectly plastic with Y=10\,GPa (dotted).
dataset/figures/0704_0008_Becmptp.eps
Principal adiabat, shock Hugoniot, and release adiabat for Be in normal stress-temperature space, neglecting strength (dashed), for Steinberg-Guinan strength (solid), and for elastic-perfectly plastic with Y=10\,GPa (dotted).
dataset/figures/0704_0008_Almelt.eps
Demonstration of shock Hugoniot solution across a phase boundary: shock-melting of Al, for different initial porosities.
dataset/figures/0704_0008_impact.eps
Wave interactions for the impact of a flat projectile moving from left to right with a stationary target. Dashed arrows are a guide to the sequence of states. For a projectile moving from right to left, the construction is the mirror image reflected in the normal stress axis.
dataset/figures/0704_0008_shockrel.eps
Wave interactions for the release of a shocked state (shock moving from left to right) into a stationary `window' material to its right. The release state depends whether the window has a higher or lower shock impedance than the shocked material. Dashed arrows are a guide to the sequence of states. F...
dataset/figures/0704_0008_doublerel.eps
Wave interactions for the release of a shocked state by tension induced as materials try to separate in opposite directions when joined by a bonded interface. Material damage, spall, and separation are neglected: the construction shows the maximum tensile stress possible. For general material properties...
dataset/figures/0704_0008_composite.eps
Schematic of uniaxial wave interactions induced by the impact of a flat projectile with a composite target.
dataset/figures/0704_0008_impactsim_notes.eps
Hydrocode simulation of Al projectile at 3.6\,km/s impacting a Mo target with a LiF release window, 1.1\,s after impact. Structures on the waves are elastic precursors.
dataset/figures/0704_0009_f12.ps
sedI0Spectral Energy Distributions of the Class I sources in the sample. The open dots signal the observed fluxes from J-band to MIPS-70 when available. The label gives the index in Table yso-table.
dataset/figures/0704_0009_f14.ps
sedF0Spectral Energy Distributions of the Flat sources in the sample. Symbols as in Figure sedI0.
dataset/figures/0704_0009_f15.ps
sedII0Spectral Energy Distributions (SED) of the Class II sources in the sample. The open and solid dots are the observed and dereddened fluxes respectively. The grey line is the stellar model of a K7 star and the dashed line is the median SED of the TTauri stars in Taurus by Hartmann et al. (2005) normalized to the de...
dataset/figures/0704_0009_f16.ps
sedII1Spectral Energy Distributions of the Class II sources in the sample (continued).
dataset/figures/0704_0009_f17.ps
sedII2Spectral Energy Distributions of the Class II sources in the sample (continued).
dataset/figures/0704_0009_f18.ps
sedII3Spectral Energy Distributions of the Class II sources in the sample (continued).
dataset/figures/0704_0009_f19.ps
sedIII0Spectral Energy Distributions of the Class III sources in the sample.
dataset/figures/0704_0009_f21.eps
diskLDistribution of disk to star luminosity ratios. The solid and dashed lines are the total sample and T Tauri-like sample of SEDs, respectively. Also marked are the typical ranges of L_ disk/L_ star ratios for debris disks, passive irradiated disks and accretion disks. The figure indicates that objects of all thre...
dataset/figures/0704_0017_hbgpspx4win_24591rvel1.ps
Radial-velocity Fourier amplitude spectra from the 1991 combined data are shown for =3000 km s^-1 and 900 km s^-1 for the ^-1 and 1200 km s^-1 for the denotes the orbital frequency of the system, 2 its first harmonic and + the upper orbital side band where is the spin frequency. The data were prewhitened by the orb...
dataset/figures/0704_0017_hapspx4win_01rvelspin1.ps
Radial velocity amplitude spectra shown for the =3500 km s^-1 and 1200 km s^-1. The vertical dashed line shows the expected position of the orbital and spin period peaks. The data were prewhitened by and are shown in the third panel from the top. A window spectrum is shown at the bottom.
dataset/figures/0704_0017_habgidltrl01-5xv.ps
2001 , , q=0.21, i=78^ and M_1=0.50 M_. The bottom panels are the reconstruction of the average-subtracted data. The fourth and bottom panels are also plotted on the same scale except for
dataset/figures/0704_0017_habg_24591-1t2wunbpr.eps
The (top panel), (middle panel) and (bottom panel) spin radial velocities of the narrow component from the 1991 combined data () and 2001 data (). The
dataset/figures/0704_0017_ha_01spinrvel3t51t2wf.ps
The spin radial velocity curves of the
dataset/figures/0704_0017_trailer_spin_01avinvar4pxv.ps
The , , 4471 trailed spectra from 2001 folded on the spin period are shown at the top panels and the average-subtracted spectra are shown at the second panels. Doppler maps constructed from the phase-invariant subtracted spectra are shown in the bottom panels. The Doppler maps were constructed using the BPM and are ...
dataset/figures/0704_0017_habgidltrl01sp-4ixv.ps
, . The lookup table is as in Figure~q:habegaidltrl.
dataset/figures/0704_0017_qexillus2e.eps
A depiction of the regions where
dataset/figures/0704_0022_time3.eps
Rigid body: We show the log-distance of the approximate solution to the unit sphere as a function of time for each of the methods. Below we show the approximate solutions as a function of time for the stochastic Taylor (blue) and Munthe-Kaas methods (magenta). The trajectory starts at the top right and eventually dr...
dataset/figures/0704_0022_casimirs040607.eps
Autonomous underwater vehicle: We show the log-distance of the approximate solution to the two Casimirs C_1= p(dotted line) and C_2=|p|^2(solid line) as a function of time for each of the methods. Below, we also show the global error as a function of stepsize.
dataset/figures/0704_0027_LLphononRes2.eps
(a) Optical phonons are lattice vibrations with an out-off-phase oscillation of the two sublattices. %(b) Interband electron-hole excitations coupling to phonon modes with different circular polarization.
dataset/figures/0704_0027_PhononPlasmonB30.eps
(a) Coupled phonon and magneto-excitons as a function of the magnetic field. Energies are in units of the bare phonon energy . Dashed lines indicate the uncoupled valley-symmetric modes, with g_A=0. (b) Mode splitting as a function of the filling factor, as may be seen in Raman spectroscopy, with the resonance conditio...
dataset/figures/0704_0030_fig1.eps
Second order contributions to the self-energy. Straight lines represent electron Green's functions of the host and wavy lines phonon Green's functions.
dataset/figures/0704_0030_fig2A.ps
fig:cmpmillis The spectral function in the static limit of the half-filled Holstein model computed at temperature T=0.08(a) using the exact solution and (b) using 2nd order IPT at a low frequency, _0=0.004. The IPT solution at this small non-zero frequency is quite close to the exact solution in the static limit. In pa...
dataset/figures/0704_0030_fig3A.ps
fig:ipt Spectral functions of the half-filled Holstein model for various electron-phonon couplings U, approximated using 2nd order perturbation theory at T=0.02 and _0=0.056(top), _0=0.5(center) and _0=2(bottom). In the low frequency limit (_0=0.125), the spectral functions are similar to those in the static limit sh...
dataset/figures/0704_0030_fig4.ps
fig:seImaginary part of the self-energy of the half-filled Holstein model when U=2 and _0=2 computed using IPT and analytically continued using MAXENT. At low temperatures the low frequency behavior is Fermi-liquid like (quadratic dependence on ) down to quite low frequencies (at very low frequencies and low temperatu...
dataset/figures/0704_0030_fig5.ps
fig:ocThe real part of the optical conductivity for a system with U=2.0 and _0=2.0 for a range of temperatures. The structure of the spectrum reflects that in the density of states (see fig fig:ipt. At low frequencies, electrons may be excited within the quasiparticle resonance. The second peak at 2.0 represents excit...
dataset/figures/0704_0030_fig6.ps
fig:resThe resistivity as a function of temperature for the Holstein model for _0=2 for varying electron-phonon coupling strengths. The resistivity is in units of e^2V/ h a^2 with V the unit cell volume and a the lattice cell spacing. The behavior reflects what is seen in the self-energy. At low temperatures the be...
dataset/figures/0704_0045_fig1a.eps
Isolated solitary wave (a) and undular bore (b) entering the variable topography/bottom friction region.
dataset/figures/0704_0045_fig2.eps
Dependence of the modulus m on the physical space coordinate x in the cases without and with bottom friction in the X-independent modulation solution.
dataset/figures/0704_0045_fig3a.eps
Left: Dependence of the mean value A in the X-independent modulation solution on the physical space coordinate x without (dashed line) and with (solid line) bottom friction; Right: Same but multiplied by the Green's law factor, h^1/4
dataset/figures/0704_0045_fig4.eps
Dependence of the surface elevation amplitude a on the space coordinate x. Dashed line corresponds to the frictionless case and solid line to the case with bottom friction.
dataset/figures/0704_0045_fig5a.eps
Left: Riemann invariants behaviour in the similarity modulation solution for the flat-bottom zero-friction case ; ().
dataset/figures/0704_0045_fig6a.eps
Characteristics behaviour for the similarity modulation solution near the leading edge ^+(): (a) families _1: d/d = v_1 and _2 : =C_2 , (b) family _3: d/d = v_3.
dataset/figures/0704_0045_fig7a.eps
a) Leading edge ^+() of non-self-similar undular bore as an envelope of pairwise merging characteristics from the families d/d=v_1 and d/d=v_2; r_3 0.
dataset/figures/0704_0045_fig8a.eps
Riemann variables behaviour in the vicinity of the leading edge of the undular bore propagating over gradual slope with bottom friction (a) Adiabatic variations of the similarity GP regime, , C_D ; (b) General case, C_D .
dataset/figures/0704_0060_fig1.eps
Comparison between experimental Coulomb excitation cross sections (solid stars with error bars) and theoretical ones, calculated either with eq. cross_2(open circles), or with eq. approx(open triangles).
dataset/figures/0704_0060_fig2.eps
Coulomb excitation cross section of ^11Be, ^11B and ^54Ni and of the 13.05 MeV sate in ^16O projectiles incident on Pb targets as a function of the laboratory energy.
dataset/figures/0704_0069_postproductionCurrent.jpg
A current comprised of parallel submanifolds smeared and cropped.
dataset/figures/0704_0082_1sol_comp.eps
Snapshots of one-soliton density profiles. The upper row is plotted for =0 at the moment t=0, with k=0, _0=1, _1=1+, _1=1.27+0.79((x,t)=-(1.57-2.54)x-(8.23-1.94)t) and =hspace*-1mmcc 4/5 -1mm&-1mm2/5\\ 2/5-1mm&-1mm1/5 -1mm. The lower row is plotted for 0 at the moment t=0, with the same parameters except for =hspace*-...
dataset/figures/0704_0082_2sol+pp.eps
Density plots of |_1|^2(a), |_0|^2(b) and |_-1|^2(c) for a mutual collision between two PS-types. The parameters used here are k=1, _0=1, _1=1.03, _3=1.05+, _1=hspace*-1mmcc 1/2-1mm&-1mm/2\\/2-1mm&-1mm0 -1mm, _3=hspace*-1mmcc 0 -1mm&-1mm/2\\/2-1mm&-1mm1/2-1mm. The velocity of the right (left) moving soliton is 2.00(-3....
dataset/figures/0704_0082_2sol+fp.eps
Density plots of |_1|^2(a), |_0|^2(b) and |_-1|^2(c) for a mutual collision between DW-type and PS-type. The parameters used here are the same as those of Fig. fig:ppcollision, except for _1=hspace*-1mmcc 2/3 -1mm&-1mm2/3\\2/3-1mm&-1mm-1/3 -1mm, _3=hspace*-1mmcc 1/2-1mm&-1mm0\\ 0-1mm&-1mm-1/2-1mm. The right (left) movi...
dataset/figures/0704_0082_2sol+ff.eps
Density plots of |_1|^2(a), |_0|^2(b) and |_-1|^2(c) for a mutual collision between two DW-types. The parameters used here are the same as those of Fig. fig:ppcollision, except for _1=hspace*-1mmcc 1/2 -1mm&-1mm/2\\/2-1mm&-1mm-1/2 -1mm, _3=hspace*-1mmcc 1 -1mm&-1mm0\\ 0-1mm&-1mm0 -1mm. The values more than 2 are colore...
dataset/figures/0704_0094_ana.ps
Analytical timing-predicted dynamical mass vs. the relative speed of two objects separated by 700 kpc after 10 4 Gyrs (three lines in increasing order for increasing time) assuming Keplerian potential of point masses. Three vertical lines indicate typical Local Group Halo mass, Baryonic mass in galaxy clusters, an...
dataset/figures/0704_0094_kap.ps
Predicted bullet cluster convergence (rescaled for sources at infinity) along the line Y=0.3X+cst connecting our two potential centroids. The model predicts a lensing signal in between that of observed weak lensing data from sources at z_s=1(Clowe et al, lower end of error bars) and the united weak lensing and strong ...
dataset/figures/0704_0094_orb-.ps
The orbit of the bullet subcluster X-ray gas (red, with present V_gas=5400 for the 10 Gyrs in the past, and pink: for the future 4 Gyrs), and the orbits of the colliding main cluster halo (blue dashes) and subhalo (black dashes) in the potential (eqs. 8-10) determined by lensing data; dashes indicate length traveled ...
dataset/figures/0704_0100_accumulation.eps
An example in which no smoothing procedure makes t|_H a Morse function on H. Here, the intersection of the crease set S of the event horizon and t=t_0 hypersurface has an accumulation point.
dataset/figures/0704_0100_isolation.eps
The smoothing procedure of the event horizon H. The gradient-like vector field on H can be constructed through a slight deformation of the null geodesic generators of H. Here, the effect of the crease set S has been replaced by that of the critical points p_1, p_2 and p_3.
dataset/figures/0704_0100_critical.eps
The local structure around the critical point p of index . It can be seen that H_t(p)+ is homeomorphic to H_t(p)- with a -handle attached.
dataset/figures/0704_0100_pot.eps
The attachment of a 1-handle and a 2-handle to a 3-manifold N creates a new 3-manifold N h^1 h^2.
dataset/figures/0704_0100_b0-handle.eps
The emergence of a black hole through a 0-handle attachment.
dataset/figures/0704_0100_w0-handle.eps
The emergence of a bubble in the black hole region by 0-handle attachment, which does not occur in the real black hole space-times.
dataset/figures/0704_0100_b1-handle.eps
The collision of a pair of black holes, creating a single black hole, is realized through 1-handle attachment.
dataset/figures/0704_0100_wn-1-handle.eps
The bifurcation of one black hole into two is represeted by an (n-1)-handle attachment. This, however, never occurs in real black hole space-times.
dataset/figures/0704_0100_handlebody.eps
The structure of -handle. The core D^\{0\} corresponds to the stable submanifold with respect to the flow generated by the gradient-like vector field, and the co-core \{0\} D^n- corresponds to the unstable submanifold.
dataset/figures/0704_0100_U.eps
The neighborhood U of p is separated by h^ into the future region, U^+, and the past region, U^-.
dataset/figures/0704_0100_bifurcate.eps
The figure on the left is a conformal diagram of the maximally extended Schwarzschild space-time. The structure of the event horizon defined with respect to the two asymptotic ends is depicted on the right, with one dimension omitted. The shaded region represents the black hole region at the critical time t=t(p). This ...
dataset/figures/0704_0100_ring_formation.eps
Black ring formation from a spherical black hole must be non-axisymmetric in real black hole space-times.
dataset/figures/0704_0106_4q-0.eps
Lowest order and leading-twist contribution to semi-inclusive DIS.
dataset/figures/0704_0106_4q-qg.eps
A typical diagram for quark-gluon double scattering with three possible cuts [central(C), left(L) and right(R)].
dataset/figures/0704_0106_4q-d-0.eps
Diagram for leading order quark-antiquark annihilation with three possible cuts [central(C), left(L) and right(R)].
dataset/figures/0704_0106_4q-d-8.eps
The complex conjugate of Fig.~fig7.
dataset/figures/0704_0106_4q-Ex-1.eps
A typical diagram for next-to-leading order correction to quark-antiquark annihilation with three possible cuts [central(C), left(L) and right(R)].
dataset/figures/0704_0106_4q-d-1.eps
The t-channel of q q gg annihilation diagram with three possible cuts, central(C), left(L) and right(R).
dataset/figures/0704_0106_4q-d-5.eps
The interference between t and u-channel of q q gg annihilation.
dataset/figures/0704_0106_4q-d-14.eps
The s-channel of q q gg annihilation diagram with only a central-cut.
dataset/figures/0704_0106_4q-d-9.eps
The interference between t and s-channel of q q gg annihilation.
dataset/figures/0704_0106_4q-d-10.eps
The complex conjugate of Fig.~fig9.
dataset/figures/0704_0106_4q-d-13.eps
s-channel q q q_i q_i annihilation.
dataset/figures/0704_0106_4q-d-2.eps
t-channel qq_i( q_i) qq_i( q_i) scattering.
dataset/figures/0704_0106_4q-d-3.eps
Interference between s and t-channel of q q q q scattering
dataset/figures/0704_0106_4q-d-4.eps
The complex conjugate of Fig.~fig3.
dataset/figures/0704_0106_4q-d-21.eps
The interference between t and u-channel of identical quark-quark scattering qq qq.
dataset/figures/0704_0106_4q-d-7.eps
Interference between final-state gluon radiation from single and triple-quark scattering.
dataset/figures/0704_0109_figure1.eps
(Color online) Upper curve in each panel with numerals indicate the distribution of first, second, third, fourth etc nearest neighbor distances of SiNW(N) as cut from the ideal Si crystal, same for structure-optimized bare SiNW(N)(middle curve) and structure optimized H-SiNW(N) (bottom curve) for N=21, 57 and 81. Verti...
dataset/figures/0704_0109_figure2.eps
(Color online) Top and side views of optimized atomic structures of various SiNW(N)'s. (a) Bare SiNWs; (b) H-SiNWs; (c) single TM atom doped per primitive cell of H-SiNW (n=1); (d) H-SiNWs covered by n TM atom corresponding to n>1. E_c, E^_c, E_b, E^_b, E_G, and , respectively denote the average cohesive energy relativ...
dataset/figures/0704_0109_figure3.eps
(Color online) Band structure and spin-dependent total density of states (TDOS) for N=21, 25 and 57. Left panels: Semiconducting H-SiNW(N). Middle panels: Half-metallic H-SiNW(N)+TM. Right panels: Density of majority and minority spin states of H-SiNW(N)+TM. Bands described by continuous and dotted lines are majority a...
dataset/figures/0704_0109_figure4.eps
(Color online) D(E,), density of minority (light) and D(E,), majority (dark) spin states. (a) H-SiNW(25)+Cr, n=8; (b) H-SiNW(25)+Cr, n=16. P and indicate spin-polarization and net magnetic moment (in Bohr magnetons per primitive unit cell), respectively.
dataset/figures/0704_0112_PHSS3_arxiv_1.eps
ETs for S=15, N=5,6,7 (nested skyboxes in blue) and ``fractal limit.''
dataset/figures/0704_0114_Fig1.eps
(Color online) Dimer pattern in the quantum disordered (VBS) phase, K/J > (K/J)_c.
dataset/figures/0704_0114_Fig2.eps
(Color online) Triplon excitation gap = ( k_AF) in various approximations. The point 0 corresponds to transition to the N\'eel phase.
dataset/figures/0704_0114_Fig3.eps
Renormalization of quantum fluctuations by resummation of a ladder series, with (interactionsy) at the vertices.
dataset/figures/0704_0114_Fig4.eps
(Color online) (a.) Singlet bound state energy E_s(black), binding energy = 2 - E_s(blue), and the triplon gap (red). (b.) Triplon density n_t. (c.) Dimer order parameters. Dashed parts of the lines represent points corresponding to rapid growth of the quasiparticle density.
dataset/figures/0704_0119_Fig1.eps
(a) The temperature dependence of electrical resistivity of CeAg_2Ge_2, inset shows the low temperature part, (b) Temperature dependence of the magnetic susceptibility together with inverse magnetic susceptibility plot, solid lines indicate the CEF fitting and (c) Magnetization of CeAg_2Ge_2 measured at T~=~2~K.
dataset/figures/0704_0119_Fig2.eps
(a) Temperature dependence of the specific heat of CeAg_2Ge_2 and LaAg_2Ge_2. The inset shows the magnetic entropy. (b) The field dependence of the specific heat of CeAg_2Ge_2 for the field applied along the easy axis of magnetization, namely [100].
dataset/figures/0704_0122_fig1.eps
Stereographic view of the spin current texture, displaying simultaneously the number and spin densities. The pancake (=0.2) is distorted and at the center the number density is depleted to give a doughnut like shape. g_d=0.2g. All spins lie in the x-y plane, i.e. a coplanar spin structure, circulating around the ori...
dataset/figures/0704_0122_fig2.eps
The r-flare texture. Left (right) column shows the cross-sectional density plots of the particle number (the corresponding spin structure). The circular profile in the x-y plane is spontaneously broken. g_d=0.2g, =0.2.
dataset/figures/0704_0122_fig3.eps
Cross sections of the particle number in Fig. fig:rflare along the x and y-axis compared with Thomas-Fermi (TF) profile for g_d=0. The profile is elongated (compressed) along the x(y)-axis.
dataset/figures/0704_0122_fig4.eps
(a) The z-flare spin texture in the cigar trap along the z-axis. The spins almost point to the z direction. In the outer regions they bent. The bright region in background corresponds to high number density. g_d=0.2g, =5.0.(b) Schematic figure to explain this spin configuration due to d-d interaction.
dataset/figures/0704_0122_fig5.eps
(a) The two-z-flare spin texture under the same parameter set (g_d=0.2g, =5.0.) as in Fig. fig:zflare with different initial spin configuration. The bright region in background corresponds to high number density. (b) Schematic figure to explain this spin structure. At the z=0 plane two oppositely aligned spins meet a...
dataset/figures/0704_0128_7530fig1.eps
2007), created using the software described in this paper and obtained from the
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Scientific Figures and Captions Dataset from research papers

This repository contains the Scientific Figures and Captions dataset, which includes approximately 4.2 million entries of scientific figures and their corresponding captions extracted from academic papers on arXiv. This dataset is intended for research purposes in the fields of computer vision and natural language processing, particularly for tasks related to image captioning and automated figure analysis.

Dataset Description

The dataset is structured as a Parquet dataframe with two columns:

  • image_filename: This column contains the relative paths to image files.
  • caption: This column contains the textual captions associated with each image.

Images are stored under dataset/figures/ and are compressed into multiple parts (.z01, .z02, ..., .z103) with a final .zip file that encompasses all parts. This format is used for efficiently handling large datasets.

Extraction Instructions

To access the images, you must first decompress the multi-part ZIP archive. Make sure you have all parts of the archive (.z01 to .z103 and the .zip file) in the same directory. Most decompression tools will recognize and handle multi-part ZIP files seamlessly.

Here is an example using the command line with unzip:

# Navigate to the directory containing the compressed parts
cd dataset/figures

# Use unzip to extract the first set of images
unzip compressedfigures.zip

# combine the second set of images
cat compressedfigures_part2* > compressedfigures_part2.tar.gz
# unzip second set of images
tar xf compressedfigures_part2.tar.gz

# You're good to go!

This will extract the contents into the dataset/figures/ directory. Ensure that you have enough storage space to accommodate the uncompressed images.

Usage Example

To use the dataset in your Python projects, you'll need to read the Parquet file into a DataFrame. Here is an example using pandas:

import pandas as pd

# Load the dataset into a DataFrame
df = pd.read_parquet('dataset.parquet')

# Display the first few entries
df.head()

Once the dataset is loaded, you can use it as follows:

from PIL import Image
import matplotlib.pyplot as plt

# Example function to display an image with its caption
def show_image_with_caption(image_path, caption):
    img = Image.open(image_path)
    plt.imshow(img)
    plt.title(caption)
    plt.axis('off')  # Hide the axis
    plt.show()

# Display the first image and its caption
first_image_path = df.loc[0, 'image_filename']
first_caption = df.loc[0, 'caption']
show_image_with_caption(first_image_path, first_caption)

Acknowledgment

Special thanks to arxiv for providing access to all of the research papers.

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